FOMITES IN INFECTIOUS DISEASE TRANSMISSION: A
MODELING, LABORATORY, AND FIELD STUDY ON
MICROBIAL TRANSFER BETWEEN SKIN AND SURFACES.
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF CIVIL AND
ENVIRONMENTAL ENGINEERING
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Timothy Ryan Julian
December 2010
© 2011 by Timothy Ryan Julian. All Rights Reserved.
Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons AttributionNoncommercial 3.0 United States License.
http://creativecommons.org/licenses/by-nc/3.0/us/
This dissertation is online at: http://purl.stanford.edu/cf347cn1097
ii
I certify that I have read this dissertation and that, in my opinion, it is fully adequate
in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Alexandria Boehm, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequate
in scope and quality as a dissertation for the degree of Doctor of Philosophy.
James Leckie
I certify that I have read this dissertation and that, in my opinion, it is fully adequate
in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Robert A Canales
Approved for the Stanford University Committee on Graduate Studies.
Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in
electronic format. An original signed hard copy of the signature page is on file in
University Archives.
iii
Abstract
This dissertation examines the factors that influence fomite-mediated (e.g., indirect
contact) transmission of viral gastrointestinal and respiratory illness. Specifically,
the dissertation investigates virus transfer between surfaces and virus recovery from
surfaces, models human-fomites interactions to estimate exposure and infection risk,
and elucidates causal links between microbial contamination and illness in child care
centers. Indirect contact transmission refers to person-to-person transmission of disease via an intermediate fomite (e.g., inanimate object acting as a carrier of infectious disease). The role of indirect contact in disease spread is poorly understood in
part because the transmission route of viral pathogens is often difficult to determine.
Transmission of respiratory and gastrointestinal viruses can occur through multiple
routes (e.g., direct contact, indirect contact, airborne, and common vehicle), and the
relative contribution of each route to total disease burden is unclear.
The first study in this dissertation examines virus transfer between skin and surfaces, a necessary step in fomite-mediated transmission of viral disease. In the study,
transfer of virus between fingerpads and fomites is explored in a laboratory setting.
Bacteriophage (fr, MS2, and φX174) are used as proxies for pathogenic virus, and
over 650 unique transfer events are collected from 20 different volunteers. The study
concludes that approximately one quarter (23%) of recoverable virus is readily transferred from a contaminated surface (e.g., a fomite) to an uncontaminated surface
(e.g., a finger) on contact. Using the large data set, the direction of transfer (from
fingerpads-to-fomite or fomite-to-fingerpad) and virus species are demonstrated to
both significantly influence the fraction of virus transferred by approximately 2-5%.
To investigate the relative importance of factors contributing to fomite-mediated
iv
transmission, a child’s risk of illness from exposure to a contaminated fomite is
modeled. Specifically, the model estimates a child’s exposure to rotavirus using a
stochastic-mechanistic framework. Simulations of a child’s contacts with the fomite
include intermittent fomite-mouth, hand-mouth, and hand-fomite contacts based on
activities of a typical child under six years of age. In addition to frequency of contact
data, parameters estimated for use in the model include virus concentration on surface; virus inactivation rates on hands and the fomite; virus transfer between hands,
fomite, and the child’s mouth; and the surface area of objects and hands in contact.
From the model, we conclude that a childs median ingested dose from interacting
with a rotavirus-contaminated ball ranges from 2 to 1,000 virus over a period of one
hour, with a median value of 42 virus. These results were heavily influenced by selected values of model parameters, most notably, the concentration of rotavirus on
fomite, frequency of fomite-mouth contacts, frequency of hand-mouth contacts, and
virus transferred from fomite to mouth. The model demonstrated that mouthing of
fomite is the primary exposure route, with hand mouthing contributions accounting
for less than one-fifth of the childs dose over the first 10 minutes of interaction.
Based on the findings from the model that concentration of virus on a fomite influences a child’s risk of illness, we investigate methods to recover virus from fomites. In
a literature review and subsequent meta analysis, we demonstrate that the outcome
currently used to describe virus contamination, positivity rate, is biased by the authors’ selected sampling methods. We follow up, in the laboratory, with a comparison
of the identified methods and demonstrate that polyester-tipped swabs prewetted in
1/4-strength Ringer’s solution or saline solution is the most efficient sampling method
for virus recovery tested. The recommended method is compatible with plaque assay and quantitative reverse-transcription polymerase chain reaction, two techniques
used to quantify virus.
The link between hand / fomite contamination and infection risk was explored in a
field study at two child care centers over four months. Both respiratory and gastrointestinal disease incidence were tracked daily, while hand and environmental surface
v
contamination were monitored weekly between February 2009 and June 2009. Microbial contamination was determined using quantitative densities of fecal indicator bacteria (e.g. Escherichia coli, enterococci, and fecal coliform) on hands and fomites as
well as presence/absence of viral pathogens (e.g. enterovirus and norovirus). Health
was monitored daily by childcare staff, who tracked absences, illness-related absences,
and symptomatic respiratory and gastrointestinal illness. The resultant data suggests
that increases in microbial contamination led to increases in symptomatic respiratory
illness four to six days later, in agreement with typical incubation periods for respiratory illness. Similarly, respiratory illness led to increases in microbial contamination
on hands during presentation of symptoms, and on fomites in the following three
days.
vi
Acknowledgments
Without the contributions of the people named below, as well as many people unnamed, the following dissertation would not have been possible.
First, to Dr. Alexandria B. Boehm who served as my advisor throughout my
time here at Stanford. It has been the utmost honor to have worked so closely with
such a brilliant and enthusiastic scientist. Dr. Boehm’s immediate understanding
of, and aid in resolving, the many obstacles I encountered along the way drove the
dissertation ever onward. Without her innumerable contributions, the work herein
would not have been possible.
Second, to Dr. James O. Leckie for the many enjoyable meetings over the years.
Our topics of discussion ranged from the intricacies of the experimental design to
Stanford sports, from data analysis to the political system. I never left Dr. Leckie’s
office without being excited by new research avenues and intrigued by his questions.
The projects within would not have been possible without the interest and expertise of my other committee members. I thank Dr. Robert A. Canales for his advice
and mentoring; he lit my interest in exposure assessment modeling and environmental statistics. I also thank Dr. Lynn M. Hildemann, whose contributions during
the research proposal phase improved the quality of the work, and motivated the
field portion. Finally, I thank Dr. Yvonne A. Maldonado, committee chair, for her
contributions to the refinement of the dissertation through her expertise in pediatric
infectious disease.
Special thanks to Dr. Paloma Beamer, both mentor and friend. While a graduate
student, Dr. Beamer proposed the application of the chemical exposure modeling
framework to biological agents; her work was the impetus for this dissertation.
vii
Much of the brain power and laboratory work embedded in the dissertation was
contributed by research colleagues and friends. Specifically, Willa AuYeung, Daniel
Keymer, Karen Knee, Royal Kopperud, Blythe Layton, Joey McMurdie, Mia Morgan, Allison Pieja, Todd Russell, Alyson Santoro, Lauren Sassoubre, Nick de Sieyes,
Francisco Tamayo, Emily Viau, Sarah Walters, George Wells, Simon Wong, Kevan
Yamahara, and Valentina Zuin. Additionally, Amy J. Pickering contributed significantly, especially to the child care center study which would not have been possible
without her seemingly inexhaustible contributions of time and effort. Thanks, also,
to Joell Hamby, Brenda Sampson, and Sandra Wetzel for administrative support.
The decision to attend graduate school at Stanford University was most influenced
by my interactions with undergraduate advisors from Cornell University. Going forward, I hope that I reflect the enthusiasm of Dr. Louis D. Albright, Dr. Rebecca L.
Schneider, and Dr. Michael B. Timmons in my approach to research and teaching.
Thanks to the many friends I am lucky to have made both before and during my
time at Stanford. Our time together, often spent camping, hiking, at dinner parties, at
rock concerts, and sharing never ending pasta bowls, has passed too quickly. Thanks
to Nathan, Naveen, and Sean for their lifelong friendships forged through heated
debates (academic, political, and otherwise) over glasses of Scotch.
To Sara, I cannot thank her enough for everything: from breakfast this morning
to love, calm, and balance every day. From help with statistical modeling to watching
music videos of The Darkness. She is my best friend.
And to my family: Thomas, Eileen, Tommy, Missy, and Dani. For my entire life,
they have provided an endless supply of encouragement, love, patience, and support.
Without them, none of this would be possible. Nor would it have been as enjoyable.
We should all be so blessed as to have such a wonderful family.
The dissertation research was funded by the Shah Family Research Fellowship for
Catastrophic Risk from Stanford University, the United States Environmental Protection Agency (USEPA) Science to Achieve Results Graduate Fellowship Program
and the UPS Endowment Fund at Stanford University. EPA has not officially endorsed this dissertation and the views expressed herein may not reflect the views of
the EPA.
viii
Contents
Abstract
iv
Acknowledgments
vii
1 Introduction
1
1.1
Fomites in Infectious Disease Burden . . . . . . . . . . . . . . . . . .
1
1.2
Transmission Routes of Infectious Disease . . . . . . . . . . . . . . .
3
1.2.1
Vectorborne Transmission . . . . . . . . . . . . . . . . . . . .
3
1.2.2
Airborne Transmission . . . . . . . . . . . . . . . . . . . . . .
4
1.2.3
Common Vehicle Transmission . . . . . . . . . . . . . . . . . .
4
1.2.4
Contact Transmission . . . . . . . . . . . . . . . . . . . . . . .
5
1.3
History of Fomite-Related Research . . . . . . . . . . . . . . . . . . .
6
1.4
Quantitative Microbial Risk Assessment . . . . . . . . . . . . . . . .
11
1.5
Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . .
13
1.6
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
1.7
Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
1.8
Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
2 Virus Transfer
22
2.1
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23
2.2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
2.3
Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . .
25
2.3.1
Volunteers . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
2.3.2
Virus and Preparation of Inoculum . . . . . . . . . . . . . . .
25
ix
2.3.3
Plaque Assay . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
2.3.4
Virus Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
2.3.5
Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
2.4.1
Virus Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
2.5
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
2.6
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
2.7
Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
2.8
Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
2.4
3 Rotavirus Exposure Model
39
3.1
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
3.2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41
3.3
Model Description
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
3.4
Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . .
45
3.4.1
Parameter Estimation . . . . . . . . . . . . . . . . . . . . . .
45
3.4.2
Model Approach . . . . . . . . . . . . . . . . . . . . . . . . .
49
3.4.3
Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . .
49
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50
3.5.1
Parameter Estimation . . . . . . . . . . . . . . . . . . . . . .
50
3.5.2
Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
3.5.3
Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . .
55
3.6
Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
56
3.7
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
3.8
Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
3.9
Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
3.5
4 Virus Recovery from Surfaces
72
4.1
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73
4.2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
4.3
Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . .
76
4.3.1
76
Review of Virus Surface Sampling Literature . . . . . . . . . .
x
4.3.2
Laboratory–Based Surface Sampling Method Comparison . . .
78
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
4.4.1
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . .
82
4.4.2
Laboratory–based Surface Sampling Method Comparison . . .
83
4.4.3
qRT–PCR . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
84
4.5
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85
4.6
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88
4.7
Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
4.8
Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
4.4
5 Health and Surfaces in Child Care Centers
96
5.1
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
97
5.2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
97
5.3
Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.4
5.3.1
Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.3.2
Surveys / Demographic Data Collection . . . . . . . . . . . . 101
5.3.3
Sampling Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.3.4
Health Data Collection . . . . . . . . . . . . . . . . . . . . . . 101
5.3.5
Hand Rinse Sampling . . . . . . . . . . . . . . . . . . . . . . . 102
5.3.6
Environmental Surface Sampling . . . . . . . . . . . . . . . . 103
5.3.7
Microbiological Methods . . . . . . . . . . . . . . . . . . . . . 103
5.3.8
Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.4.1
Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.4.2
Health Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.4.3
Hand Rinse Samples . . . . . . . . . . . . . . . . . . . . . . . 107
5.4.4
Environmental Samples . . . . . . . . . . . . . . . . . . . . . . 108
5.4.5
Health Associations with Hand and Surface Contamination . . 108
5.5
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.6
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.7
Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
xi
5.8
Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6 Conclusions
126
6.1
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
6.2
Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
A Supplemental Material for Chapter 3
134
B Supplemental Material for Chapter 4
137
B.1 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
C Supplemental Material for Chapter 5
150
C.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
C.1.1 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
C.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
C.2.1 Bivariate Correlations . . . . . . . . . . . . . . . . . . . . . . 152
C.2.2 Hand Contamination and Health Data. . . . . . . . . . . . . . 152
C.2.3 Hand Contamination and Environmental Contamination. . . . 153
C.2.4 Environmental Contamination and Health Data. . . . . . . . . 153
C.2.5 Health Associations with Hand and Surface Contamination . . 154
C.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
C.3.1 Use of Multiple Comparisons
. . . . . . . . . . . . . . . . . . 154
C.4 Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
xii
List of Tables
1.1
Gastrointestinal and Respiratory Viruses Transmitted Via Fomites . .
17
1.2
Evidence of Viruses in Fomite Transmission . . . . . . . . . . . . . .
18
2.1
Descriptive Statistics of Fraction Transferred for Each Subset. . . . .
35
2.2
Distribution Parameters for Fraction Transferred by Phage Type. . .
36
3.1
Input Parameters and Estimated Values for Exposure Model . . . . .
61
3.2
Sensitivity Analysis of Exposure Model . . . . . . . . . . . . . . . . .
62
4.1
Eluents Used to Remove Virus from Fomites . . . . . . . . . . . . . .
92
4.2
Implements Used to Remove Virus from Fomites . . . . . . . . . . . .
93
4.3
Comparison of Recovery of Infective Phage . . . . . . . . . . . . . . .
94
4.4
Surface Material, Implement, and Eluent Influence on Recovery . . .
95
5.1
Summary of Environmental Fomites Samples. . . . . . . . . . . . . . 115
5.2
Pathogen Detection PCR Parameters . . . . . . . . . . . . . . . . . . 116
5.3
Child Care Center Population Demographics . . . . . . . . . . . . . . 117
5.4
Child Care Center Population Health and Hygiene Knowledge . . . . 118
5.5
Frequency of Absenteeism and Symptomatic Illness in Child Care Centers119
5.6
Respiratory Illness as Function of Enterococci on Surfaces . . . . . . 120
B.1 Summary of Studies in Literature Review. . . . . . . . . . . . . . . . 147
B.2 Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
B.3 Positivity Rate Metrics By Virus . . . . . . . . . . . . . . . . . . . . 149
C.1 New Illness Episodes Model . . . . . . . . . . . . . . . . . . . . . . . 157
xiii
C.2 Illness-Related Absences Model . . . . . . . . . . . . . . . . . . . . . 158
xiv
List of Figures
1.1
Infectious Disease Transmission Routes . . . . . . . . . . . . . . . . .
20
1.2
Steps Required for Fomite-Mediated Transmission . . . . . . . . . . .
21
2.1
Histograms of Fraction Virus Transferred . . . . . . . . . . . . . . . .
38
3.1
Schematic Model of Virus Transfer . . . . . . . . . . . . . . . . . . .
64
3.2
Simulated Timing of Contacts . . . . . . . . . . . . . . . . . . . . . .
65
3.3
Simulated Concentration, Exposure, and Dose Profiles
. . . . . . . .
66
3.4
Temporal Trends in Concentration and Exposure Profiles . . . . . . .
67
3.5
Dose and Risk Boxplots . . . . . . . . . . . . . . . . . . . . . . . . .
68
3.6
Dose and Infection Risk as Function of Virus Concentration . . . . .
69
3.7
Temporal Trends in Dose . . . . . . . . . . . . . . . . . . . . . . . . .
70
3.8
Temporal Sensitivity Analysis of Fraction Transferred and Contact Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
71
4.1
Fraction of Virus Recovered By Implement Eluent Combinations . . .
90
5.1
Time Series of Absences at Child Care Centers . . . . . . . . . . . . . 122
5.2
Time Series of Reported Symptoms at Child Care Centers . . . . . . 123
5.3
Time Series of Bacteria on Hands in Child Care Centers . . . . . . . 124
5.4
Time Series of Bacteria on Fomites in Child Care Centers . . . . . . . 125
xv
Chapter 1
Introduction
1.1
Fomites in Infectious Disease Burden
Informed and successful disease control choices are made on the basis of understanding infectious agent transmission routes (Hurst, 1996). Perhaps the most well-known
example is also the first: Dr. John Snow’s removal of a water pump handle in London in 1854 halted a cholera epidemic. Similar contemporary interventions tailored to
impede transmission include hygiene education (Aiello et al., 2008), improved water
quality at the source and in the home, improved sanitation (Fewtrell et al., 2005),
social distancing (Glass et al., 2006), and respiratory masks (Jefferson et al., 2009).
The success of the interventions relies, in part, on the prior justification that the
transmission route is a major contributor to the overall disease burden. Contemporary understanding is that, in particular for respiratory and gastrointestinal virus,
transmission is complex, occurring via multiple, likely interrelated, routes (Goldmann,
2000; Nicas and Sun, 2006).
Understanding transmission of respiratory illness (RI) and gastrointestinal illness
(GI) disease spread, and how to prevent it, will aid reductions in burden. Annually,
the average adult has about 2 to 4 acute upper respiratory illnesses. Children have
approximately 6 to 8 (Heikkinen and Jrvinen, 2003). Per year, there are 400 million
cases of lower respiratory infections, which compared to upper respiratory illnesses,
are more likely to lead to hospitalization and death (Monto, 2002; Mathers et al.,
1
CHAPTER 1. INTRODUCTION
2
2008). Gastrointestinal illness is responsible for 4.5 billion cases annually, leading
to an estimated 1.7 million deaths in children under five every year (Mathers et al.,
2008). Combined, acute respiratory infections and diarrheal disease account for 34%
of the 10.4 million annual deaths among children under five (Mathers et al., 2008).
Viruses, especially those presented in Table 1.1, are commonly responsible for respiratory and gastrointestinal illlness. Although medical treatments, like antibiotics,
oral rehydration therapy, and zinc supplementation, are proving very effective (Hahn
et al., 2001; Lazzerini and Ronfani, 2008; Roth et al., 2010), one estimate suggests
that universal implementation of these methods can only reduce child mortality by an
additional 20% (Jones et al., 2003). Combining medical treatments with prevention
of disease spread can further reduce RI and GI morbidity. Reductions through prevention of disease spread require an understanding of transmission routes. However,
the transmission of RI and GI is complex.
Contributing to the complexity is an incomplete understanding of indirect contact
transmission. Indirect contact transmission refers to person-to-person transmission of
disease via an intermediate fomite (i.e., inanimate object acting as a carrier of infectious disease). Indirect contact, or fomite-mediated contact, is poorly understood due,
in part, to the nature of the transmission route. There are a number of ways fomites
can be contaminated with infectious disease, including contact with bodily fluids,
body parts, or other fomites and settling from airborne particles by talking, sneezing,
coughing, or vomiting (Hota, 2004; Boone and Gerba, 2007). Contamination of a
fomite may provide no obvious or visible evidence of infectious disease presence. Additionally, the routes by which an infectious agent contaminates a fomite are equally
able to infect a susceptible individual without the intermediate fomite. Therefore, it
is often difficult to determine whether a transmission event occurred directly between
an infected host and a susceptible host, or the event occurred indirectly via a fomite.
Moreover, the factors that influence fomite-mediated transmission, as well as their
relative importance, are poorly understood. Not only is contamination of a fomite
a requisite step in indirect contact transmission, but viral persistence and transfer
to a susceptible individual are also required. To initiate infection via fomites, a
virus must be able to contaminate a fomite, persist on the fomite, come into contact
CHAPTER 1. INTRODUCTION
3
with a susceptible host, and to initiate infection in the susceptible host. Common
questions concerning RI and GI viruses include whether or not an etiological agent is
capable of fomite-mediated transmission, and with what efficiency relative to other
routes. Characteristics of viruses relevant to transmission via fomites are provided in
Table 1.2.
The dissertation presented here contributes to fundamental knowledge concerning
the factors that influence fomite-mediated transmission for viral disease. To better understand fomite-mediated transmission, this chapter provides a background on
infectious disease transmission routes, a history of research relevant to the contemporary understanding of fomites, and a description of quantitative risk assessment
modeling. Modeling is a tool frequently used to assess factors that influence risk of
illness in transmission of communicable infectious disease.
1.2
Transmission Routes of Infectious Disease
Fomite-mediated transmission is a subset of contact transmission, one of the major
routes of infectious disease transmission. There are, arguably, four major routes: vectorborne, airborne, common vehicle, and contact (See Figure 1.1) (James and David,
2001; Mangili and Gendreau, 2005). The major transmission routes are not mutuallyexclusive. Rather, an etiological agent may utilize multiple routes to transfer between
infected and susceptible hosts. Similarly, the major routes are not necessarily distinct
categories. As an example, indirect contact transmission during preparation may result in a foodborne (i.e., common vehicle) outbreak.
1.2.1
Vectorborne Transmission
Vectorborne transmission is similar to fomite-mediated transmission only insofar as
to replace the role of inanimate objects with a living vector. Although fomites are
occasionally considered vectors (Lemon et al., 2008), this is not a strictly accurate
definition (James and David, 2001; Mangili and Gendreau, 2005). That is, vectorborne transmission is the transfer of an infectious agent to a susceptible host via an
CHAPTER 1. INTRODUCTION
4
arthropod or vermin intermediary. Vectorborne diseases are among the top twenty
most common causes of death, worldwide, due in part to the ubiquity of malaria in
low income countries (Mathers et al., 2008). The vector carriage of the infectious
agent may simply be a case of mechanical transfer (like the role of a fomite), or
the agent may undergo biological transformations during carriage. Examples of the
former include dengue virus, west Nile virus, and yellow fever; of the latter include
malaria and African trypanosomiasis. In research investigating fomites as causative
agents in transmission of vectorborne diseases (specifically dengue virus and yellow
fever), no individual exposed to fomites was infected (Ashburn and Caraig, 1907).
1.2.2
Airborne Transmission
A second route of transmission is airborne, which typically refers to the aerosolization and movement, over long distances, of pathogens from an infected individual to
a susceptible host. Airborne transmission often plays an important role in the contamination of fomites, as initially aerosolized particles may settle onto surfaces (Nicas
and Sun, 2006). Similarly, resuspension from contaminated fomites may contribute
to airborne transmission (Nicas and Sun, 2006). A common phenomenon in airborne
viral transmission is the formation of viral droplet nuclei. Viral droplet nuclei are
formed due to water evaporation from expiration by an infected host. At less than
5 µm in diameter, the viral droplet nuclei can remain suspended for long periods
(Lowen et al., 2007). Recent evidence suggests that low humidity leads to increased
formation of viral droplet nuclei, and therefore more efficient transmission (Lowen
et al., 2007). The formation of viral droplet nuclei, however, is not a requirement
for airborne transmission. Bacteria, for example, are capable of being transmitted
via the airborne route. Tuberculosis is an example of an airborne bacteria (Mathers
et al., 2008).
1.2.3
Common Vehicle Transmission
Common vehicle transmission is often intertwined with fomite-mediated transmission,
but refers more specifically to the potential infection of multiple individuals via a
CHAPTER 1. INTRODUCTION
5
single carrier. Forms of common vehicle transmission include foodborne, waterborne,
and iatrogenic transmission. For common vehicle transmission to occur, the vehicle
needs to be contaminated prior to distribution to susceptible hosts. Food is often
contaminated in the environment prior to harvest, during processing for distribution,
or during preparation (e.g., inadequate hygiene or cooking) (Bresee et al., 2002).
Water, both recreational and drinking, is often contaminated in the environment, as
could occur due to poor sanitation, hygiene, and/or inadequate sewage or storm water
control (Craun et al., 2006). Drinking water, even if it was previously treated, can
be contaminated during delivery and/or storage. Examples of vehicles in iatrogenic
transmission, or transmission during medical procedure, include nonsterile injection
needles or catheters (Khan et al., 2000; Luijt et al., 2001), and/or infected blood or
organs (Iwamoto et al., 2003).
Fomites frequently contribute to infectious disease outbreaks that occur via common vehicle transmission. As an example, contamination of food during processing
or preparation can occur due to contact with a contaminated surface, like a cutting
board. Similarly, infection of a patient by using a nonsterile injection needle could
also be considered fomite-mediated transmission.
1.2.4
Contact Transmission
Fomite-mediated transmission is most often included in the fourth major mode of
transmission: contact. Contact transmission occurs most often either through direct
physical contact, which includes both casual (e.g., touching, kissing) and sexual contact, or indirect contact via a fomite. Two other forms of contact transmission are
vertical transmission, which is defined as the transfer of disease from a mother to her
fetus either in utero or during child birth, and zoonotic transmission, which is the
transfer of disease between vertebrate animals and humans.
Often, the distinction between contact and other transmission routes is blurred.
Vectorborne transmission, as an example, is sometimes included as a subset of contact
transmission, especially when vector carriage is simply mechanical (i.e., no biological
transformations of the pathogen occur in the host) (Hurst, 1996). Similarly, the
CHAPTER 1. INTRODUCTION
6
production of large respiratory droplets during expirations from talking, coughing,
or sneezing blurs the distinction between contact and airborne transmission. Large
droplets can be spread over short distances and intercepted by a susceptible host or
fomite while settling. This form of transmission is sometimes considered a unique
transmission route (Friedman and Petersen, 2004), and other times considered a form
of contact transmission (Baron and Jennings, 1991; Hurst, 1996). Large droplet
settling is one way respiratory pathogens contaminate fomites, contributing to the
possibility of transmission via indirect contact (Nicas and Sun, 2006).
Contamination of an inanimate object is only the first step in transmission via
fomites (See Figure 1.2). Another requirement is that the etiological agent must
remain viable on the fomite for a period sufficient for the fomite to come into contact
with a susceptible individual. If it is able to persist long enough, the agent must then
be able to transfer from the fomite to a point of entrance on a susceptible individual.
For respiratory and gastrointestinal diseases, the point of entrance is most often a
mucous membrane, such as through the mouth, nose, ears, or eyes. Once transferred,
the agent must be able to initiate infection. In summary, the characteristics of the
etiological agent, the fomite, the infected individual, and the susceptible host, as
well as the interactions between the individuals and the fomite, influence efficacy of
fomite-mediated transmission.
1.3
History of Fomite-Related Research
Indirect contact via fomites was first identified by Italian physicist and scholar Girolamo Fracastoro, in 1546 (Ravenel, 1931; Clendening, 1960). Fracastero did so in his
description of distinct transmission routes. His description included direct contact,
indirect contact, and a predecessor to aerosolization (specifically, the “transmi[ssion of
a disease] to a distance...merely by looking”) (Clendening, 1960). Fracastero posited
that the etiology of a disease determined the transmission route, using specific examples of contemporary diseases (e.g., scabies via indirect contact, and smallpox via
aerosolization). He also posited that contaminated fomites may remain so for “two to
three years”, and described porous objects (“linen, cloth, and wood”) as more likely
CHAPTER 1. INTRODUCTION
7
to act as fomites than nonporous ones (“iron, stone”) (Clendening, 1960). In so doing, Fracastero laid the groundwork for the concept that inanimate objects contribute
to disease. The mechanism by which fomites acted remained elusive as Fracastoro’s
work predated Agostini Bassi’s germ theory (1835) by almost 300 years.
The knowledge that inanimate objects could spark disease outbreaks (in fact, the
Latin definition of “fomes” is “tinder”) provided an opportunity for one of the most
well-known examples of biological warfare in the New World. British colonialists,
during their efforts to combat the Natives in the 18th century, provided the Natives
with blankets and handkerchiefs from hospitals to “innoculate” them with smallpox
(Fenn, 2000). Epidemics that tore through Native populations in 1763 and 1764 likely
resulted from the provision of the blankets (Fenn, 2000).
The knowledge of fomites also aided in the prevention of outbreaks. As an example during the height of a plague outbreak in 1835 in Alexandria, Egypt, the British
Privy Council quarantined all ships carrying Egyptian cotton into England (Thompson, 1847; Plunket, 1879). The cotton was described as a “fomes”, referring to the
etiological agent perceived to be the cause of both the Egyptian epidemic and an
earlier epidemic in London that had occurred in 1665. To prevent the epidemic, the
cotton bales were to be “rip[ped] open” to “purify... the cotton” through exposure
“to sunlight and air” (Plunket, 1879).
In the following decades, yellow fever outbreaks on ships led to the incorrect attribution of fomites as the causative transmission route. The prevailing evidence was
the occurrence of outbreaks on ships weeks after they had set sail (Bell, 1901). As no
sailor was symptomatic at launch, or in the days leading up to symptoms, sailors and
scientists attributed the outbreaks to contact with objects such as “personal clothing and books” (Bell, 1901). The implication of fomites prevailed until randomized
control trials conducted by Dr. Walter Reed (based on work first posited by Carlos Finlay) proved mosquitoes were the vector for yellow fever (Clendening, 1960).
Specifically, Dr. Reed discovered that a period of approximately 12 days needed to
pass for a mosquito that had consumed blood of an infected individual to be able to
infect another (Bell, 1901). The uncertainty of the route of yellow fever transmission
mirrors contemporary uncertainty in transmission routes. As an example, respiratory
CHAPTER 1. INTRODUCTION
8
illness was perceived to be transmitted only through airborne transmission as little
as forty years ago.
Among the first work to rigorously test the hypothesis that a respiratory virus
could be transmitted via contact came in the 1970s. J. Owen Hendley and Jack
Gwaltney of the University of Virginia proposed that direct and indirect contact
contributed to transmission of rhinovirus, perpetrator of over one third of the cases
of the “common cold” (Hendley et al., 1973). Hendley and Gwaltney showed that
rhinovirus was shed from an infected patient, capable of surviving outside the host on
an environmental surface, and capable of infecting a susceptible individual who had
contacted the contaminated surface (See Figure 1.2) (Hendley et al., 1973). Their
work was among the first to delineate the steps necessary for a pathogenic agent to
be transmitted via fomites. Laboratory work confirming the ability of rhinovirus to
be transmitted via direct (hand-to-hand) and indirect (hand-to-surface) contact soon
followed (Gwaltney et al., 1978; Gwaltney, 1982).
The work by Hendley and Gwaltney was also among the first to demonstrate, conclusively, that fomites were a viable route for a respiratory pathogen. Prior to their
work, symptoms of coughing and sneezing associated with rhinovirus were thought to
contribute to its spread via airborne transmission (Hendley et al., 1973). Other studies, though, had tried and failed to demonstrate airborne transmission of rhinovirus
(Hendley et al., 1973).
Around the same time, interest in nosocomial infections was on the rise, particularly for a common respiratory pathogen (respiratory syncytial virus, or RSV).
Caroline Hall and R. Gordon Douglas, Jr. of the University of Rochester, citing the
early work of Hendley and Gwaltney, recognized that fomites may be contributing to
the spread of RSV in hospitals (Hall et al., 1981). In a series of papers investigating
RSV transmission via fomites, Hall et al. (1980); Hall and Douglas Jr (1981); and
Hall (1983) demonstrated that RSV is capable of following the necessary steps to be
transmitted via fomites. That is, RSV survives on surfaces, is readily transferred between surfaces and hands, and can infect susceptible hosts when a contaminated hand
contacts their nose or eyes. Scaling up from the laboratory to the field, Hall et al.
(1981) also examined the risk of handling infected infants at various levels of contact
CHAPTER 1. INTRODUCTION
9
and demonstrated that large droplet contact and indirect contact with fomites were
more efficient routes of RSV transmission than small particle aerosolization (Hall
et al., 1981).
The work by Hendley and Gwaltney along with the work of Hall and Douglas
contributed to a renewed interest in fomites in infectious disease transmission as they
demonstrated that fomites were an integral transmission route in diseases previously
perceived to be primarily airborne. In the following decades (1980s-1990s), research
on fomites increased four-fold, with studies examining their role in the transmission
of respiratory (Dick et al., 1987; Brady et al., 1990), gastrointestinal (Butz et al.,
1993; Wilde et al., 1992) and even bloodborne pathogens (Ferenczy et al., 1989).
The work during this decade followed closely the work of Hendley, Gwaltney, Hall,
and Douglas, in that it examined pathogen presence/absence on surfaces (Keswick
et al., 1983; Piazza et al., 1987; Wilde et al., 1992), persistence (Keswick et al., 1983;
Ansari et al., 1988; Abad et al., 1994), transfer (Jennings et al., 1988; Ansari et al.,
1988), and the relative efficiency of the indirect route of transmission (Dick et al.,
1987). The general acceptance of the role of fomites in infectious disease transmission
was highlighted, perhaps, during this period with the First European Meeting of
Environmental Hygiene in Dusseldorf in 1987.
Research on fomites began to abate in the mid-1990s. The focus of most of the
published articles on fomites during this time period continued to be their role in
nosocomial infections (McCluskey et al., 1996; Bures et al., 2000; Neely and Sittig,
2002; Das et al., 2002). Additionally, new work was published investigating the role
of fomites in animal diseases (Pirtle and Beran, 1996; Otake et al., 2002) as well as on
tracking the role of fomites in GI and RI outbreaks (Cheesbrough et al., 2000; Rogers
et al., 2000; Abad et al., 2001; Barker, 2001; Evans et al., 2002; Das et al., 2002). The
latter proved influential in the resurgence of fomites research over the past several
years (2004-2010).
In particular, growing concern over two communicable diseases, norovirus (a gastrointestinal virus) and influenza (a respiratory virus), contributed to a resurgence in
fomites research. Norovirus, first idenfied in 1972 (Kapikian et al., 1972), is the most
CHAPTER 1. INTRODUCTION
10
common cause of gastroenteritis in the United States due, in part, to its extreme contagiousness (as few as 1 viral particles may be needed to cause illness (Teunis et al.,
2008), and there are as many as 14 secondary cases for each primary case (Heijne
et al., 2009)). Evidence suggests that direct and indirect transmission are important
routes for norovirus transmission. For example, the U.S. Centers for Disease Control
and Prevention report that 16% of norovirus cases are caused by person-to-person
spread (Norovirus: Technical Fact Sheet, http://www.cdc.gov/ncidod/dvrd/revb
/gastro/norovirus-factsheet.htm, accessed Sep 2010). Similarly, a series of studies
of outbreaks traced the source to environmental contamination of norovirus (Cheesbrough et al., 2000; Evans et al., 2002). Over the last decade, researchers have sought
to further investigate the relevance of fomites in norovirus outbreaks (Duizer et al.,
2004; Clay et al., 2006; Jones et al., 2007; Girard et al., 2010).
Similarly, outbreaks of influenza have increased interest in research on the potential role of fomites in transmission. Contemporary thought supports that aerosolization of small particles, including viral droplet nuceli formation and large droplet
contact, are the primary transmission routes (Lowen et al., 2007; Tellier, 2009). Like
rhinovirus and RSV, influenza is a respiratory virus. Nevertheless, the contribution
of fomites continues to be debated (Brankston et al., 2007). Research to characterize
the role of fomites, prompted by the concern over a future pandemic, has mirrored
the early work on both rhinovirus and RSV. Specifically, published work has documented influenza survival on surfaces (Thomas et al., 2008; Sakaguchi et al., 2010),
disinfection (Rudnik et al., 2009; Weber and Stilianakis, 2008), detection on surfaces
(Boone and Gerba, 2005), and the relative efficacy of fomes-mediated transmission
relative to other routes (Brankston et al., 2007; Weber and Stilianakis, 2008; Tellier,
2009). Much of this work has been done in the context of tailoring interventions to
reduce infectious disease burden during a pandemic.
In total, the research dedicated to fomites has concluded that indirect contact is
an important route for transmission of respiratory and gastrointestinal illness. Nevertheless, better quantitative data is needed. Research over the last half century has
delineated the steps required for an etiological agent to be efficiently transmitted
via fomites. Laboratory-scale studies, typically focusing on specific pathogens, have
CHAPTER 1. INTRODUCTION
11
demonstrated and quantified organisms’ abilities to transfer to and from fomites,
persist on fomites, and to remain infectious. Studies scaling up to the field have
demonstrated that contaminated fomites can initiate infection, and have also assessed
the efficacy of fomite-mediated transmission relative to other routes. Nevertheless, a
better characterization of the factors required for fomite-mediated transmission, and
their relationships, is needed. In fact, in a review of the role of fomites in transmission of respiratory and enteric viruses, the authors (Boone and Gerba, 2007) noted
the need for “better quantitative data”. Specifically, the Boone and Gerba identified
the need for better data on viral inactivation rates, viral transfer between surfaces,
and viral distribution and concentration on surfaces. The purpose of better data is
to improve and develop “risk assessment models that associate viral infection with
fomite contact” (Boone and Gerba, 2007).
1.4
Quantitative Microbial Risk Assessment
Contributing to the Boone and Gerba (2007) assertion that data are needed for risk
assessment models was the development, in the mid-1990s, of the framework for applying the reductionist approach of quantitative risk assessment (QRA) to infectious
disease. QRA is the “technical assessment of the nature and magnitude of a risk
caused by a hazard” (Jaykus et al., 1996) where the hazard can include “substances,
processes, action, or events” (Covello and Merkhofer, 1993). QRA was first developed
in the 1970s and later formalized with the benchmark publication “Risk Assessment
in the Federal Government, Managing the Risk”, known colloquially as the Red Book,
by the U.S. National Academy of Sciences in 1983 (NRC, 1983). Among the first applications of the QRA framework to assess infectious disease risk were assessments of
waterborne transmission (Haas, 1983; Gerba and Haas, 1988; Regli et al., 1991; Rose
et al., 1991), which led to the development of a codified framework for quantitative
microbial risk assessment by the International Life Sciences Institute (ILSI) in 1996
(ILSI, 1996) and revisited in 1999 (ILSI, 1999).
The framework for quantitative microbial risk assessment (QMRA) is adapted
from the QRA paradigm. The ILSI framework for microbial risks consists of three
CHAPTER 1. INTRODUCTION
12
phases: 1) problem formulation, 2) analysis, and 3) risk characterization (ILSI, 1996,
1999). Problem formulation “identifies the goals, breadth, and focus of the risk assessment, the regulatory and policy context of the assessment, and the major factors”
(ILSI, 1999). Analysis develops exposure and dose-response assessments to work toward risk characterization, which is a quantitative characterization of the likelihood,
type, and magnitude of human health effects (ILSI, 1999). Risk characterization also
incorporates a transparent accounting of uncertainty or variability contributions to
the final risk estimates (ILSI, 1999). A fourth phase (risk management) is occasionally included in the risk assessment paradigm and encompasses the risk mitigation
and communication strategies (Haas et al., 1999; Covello and Merkhofer, 1993). A
major contribution of the ILSI framework for microbial risks was its emphasis on the
“dynamic and iterative process of the risk assessment process”, and that findings in a
later stage (e.g., risk characterization) should be used to refine and improve findings
from an earlier stage (e.g., analysis).
The paradigm for QRA as outlined in the Red Book for human health effects
was developed to account for risk from chemical exposures (NRC, 1983; Haas et al.,
1999). To adapt QRA to microbial hazards, complexities unique to pathogens need
to be considered (ILSI, 1996). The complexities include: 1) growth and/or inactivation of pathogens, 2) non-heterogeneous pathogen distributions in environmental
matrices, 3) naturally or artificially acquired immunity, 4) asymptomatic infection,
5) secondary transmission (e.g., spread from an infected individual), 6) multiple endpoints (e.g., infection, illness, mortality), 6) potential for multiple exposure routes,
and 7) uncertainty in environmental concentration measurements (e.g., accuracy of
detection methods) (ILSI, 1996; Haas et al., 1999). In 1999, many of the first practitioners of QMRA (Haas et al., 1999) summarized and applied the QMRA paradigm to
examples from many of the major transmission routes in the first and only textbook
on the topic, Quantitative Microbial Risk Assessment.
Despite the evidence that fomites play an important role in the transmission of disease, few studies have applied the framework of QMRA to model risk from fomites. In
those that have, the estimated risk typically relies on simplistic exposure assessments
that model human interaction with fomites based on estimates of the probability
CHAPTER 1. INTRODUCTION
13
that a contact event occurs (e.g., 10% chance a fomite is contacted by hand), a constant frequency of the contact event (e.g., mouth is contacted by hand 0.08 times per
minute), or a constrained sequence of events (e.g., fomes touches hand, hand then
touches mouth) (Gibson et al., 1999; Chen et al., 2001; Gibson et al., 2002; Haas
et al., 2005; Nicas and Sun, 2006; Nicas and Best, 2008). Examples of fomite-related
quantitative microbial risk assessments include: 1) estimating risk rotavirus infection
from clothes laundering (Gibson et al., 1999), 2) estimating risk from contaminated
surfaces in health care settings (Nicas and Sun, 2006), 3) estimating risk of cross
contamination during food preparation (Chen et al., 2001), and 4) estimating risk
reductions acheived through hand hygiene (Gibson et al., 2002). Although the studies provide an important first step toward understanding the factors that influence
fomite-mediated transmission, they function as simplified models and do not fully
account for complexities of human-fomites interaction in field settings.
1.5
Dissertation Organization
This dissertation consists of six chapters devoted to furthering knowledge of the factors that contribute to fomite-mediated infectious disease transmission. This introduction chapter (Chapter 1) provides background on the role of fomites in disease
transmission. The four middle chapters (Chapters 2-5) present original research in
the form of stand-alone manuscripts, each with its own introduction, methods, results,
and discussion sections. The final chapter (Chapter 6), provides general conclusions
and areas for future research. The references used throughout the dissertation are
merged and appear at the end. Co-authors, along with their contributions to each
chapter, are listed at the beginning in an introductory paragraph. I am first author
on all publications that have been or will be generated from the work included in this
dissertation as I was the primary person responsible for planning, conducting, and
writing each project.
If fomites play a significant role in viral disease transmission through hand contact,
virus must transfer from contaminated fingers to fomites and transfer from fomites
CHAPTER 1. INTRODUCTION
14
to fingers of a susceptible host. This sequence of events was explored through a laboratory experiment using three bacteriophage species as proxies for pathogenic virus.
In Chapter 2 we demonstrate that approximately one quarter (23%) of recoverable
virus is readily transferred from a contaminated surface (e.g., a fomite) to an uncontaminated surface (e.g., a finger) on contact. The chapter demonstrates, using a
robust data set, that the direction of transfer (from fingerpads-to-fomite or fomite-tofingerpad) and bacteriophage species both influence the fraction of virus transferred
by approximately 2-5%. The study also suggests that hand washing reduces the fraction of virus transferred on contact due, potentially, to altered skin properties. This
mechanism may explain decreases in illness during handwashing interventions, along
with the current explanation that handwashing reduces pathogenic virus and bacteria
on the hands. In addition to implications concerning hand hygiene effectiveness, the
developed data set contributes to work on quantitative microbial risk assessments
examining fomites in disease transmission.
In Chapter 3 we combine data sets from the previous chapters with a literature
review to create a novel exposure and risk assessment model. The model, based on
a stochastic-mechanistic framework using a simulation of a child’s interaction with a
fomite, is among the first to incorporate detailed descriptions of sequential time series data modeling human-environment interaction in a microbial risk assessment. A
combined sensitivity and uncertainty analysis identifies the factors that most significantly influence risk of infection. Although the analysis demonstrates that parameters
describing human interaction are influential, uncertainty of, and variability in, virus
concentration on fomites is shown to dominate risk of exposure, and therefore infection.
To improve estimates of microbial contamination on surfaces, we compare methods
used to recover virus from fomites in Chapter 4. The literature review and subsequent meta analysis demonstrate that the outcome currently used to describe virus
contamination, positivity rate, is biased by the authors’ selected sampling methods.
In the review, we identify the most promising virus recovery methods. We follow up,
in the laboratory, with a comparison of the identified methods and demonstrate that
polyester-tipped swabs prewetted in 1/4-strength Ringer’s solution or saline solution
CHAPTER 1. INTRODUCTION
15
should be the standardized method for virus recovery. The recommended method is
compatible with two common techniques used to quantify virus from the environment,
plaque assay and quantitative reverse-transcription polymerase chain reaction. Quantification of virus from fomites is an important direction for future research, as few
identified papers on virus surface contamination have quantified virus, or indicators
of virus, contamination.
Chapter 5 examines the relationship between microbial contamination on surfaces
and adverse health outcomes in child care centers in Northern California. For four
months in 2009, we quantified fecal indicator bacteria on hands and surfaces twice
weekly at two child care centers. We simultaneously collected data on child absences
and observable symptoms of gastrointestinal and respiratory infection. Using statistical modeling, we demonstrate that increased surface contamination both leads and
lags observable respiratory symptoms. The study is among the first to infer, using
longitudinal data, a causal link between indoor microbial contamination and health
outcomes.
The research presented in the dissertation addresses the role of fomites in infectious
disease transmission. The dissertation also contributes to the development of ideas for
future research directions. In Chapter 6, new hypotheses generated over the course
of the dissertation are discussed. Also in the final chapter is a general conclusion on
the role of fomites in disease transmission.
1.6
Acknowledgments
The author acknowledges Sara J. Marks and the Stanford University School of Engineering Technical Communication Program for suggestions to improve the chapter,
as well as to the website www.dezignus.com for hosting the royalty-free vector images
of people used in Figure 1.1 and Figure 1.2.
CHAPTER 1. INTRODUCTION
1.7
Tables
16
-2
0
before symp.
0
0
0
-6
0
0
0
2-5
1-3
2-4
3-7
1-2
13
+7
4-21
3-7
up to 21
8
20
14-21
2-4
4-7
Infectious Period (d)
Onset
Conclusion
2-14
0.5-4
7-14
0.5-2
1-3
Incubation
(d)
airborne, contact
airborne, contact
contact, airborne
contact, airborne
contact, airborne
airborne, common vehicle, contact
common vehicle, contact
common vehicle, contact
common vehicle, contact, airborne
common vehicle, contact
Routes
Table 1.1: The epidemiological characteristics of common gastrointestinal and respiratory viruses transmitted via
fomites. Infectious period onset refers to the number of days prior to presentation of symptoms whereby shedding
occurs, with 0 representing same day as symptoms. The list of routes appear in order of most to least efficient, as
currently understood. Data adapted and compiled from reviews by (Boone and Gerba, 2007; Donowitz, 1999)
Gastrointestinal
Adenovirus
Astrovirus
Enterovirus
Norovirus
Rotavirus
Respiratory
Coronavirus
Influenza
Parainfluenza
Respiratory Syncytial Virus
Rhinovirus
Virus
CHAPTER 1. INTRODUCTION
17
90-200
120-300
30
Parainfluenza
RSV
Rhinovirus
icosahedral
spherical
icosahedral
filamentous
spherical
spherical
icosahedral
icosahedral
icosahedral
icosahedral
icosahedral
Shape
6.4 - 6.8
n.a.
n.a.
5-7
n.a.
8
5.5-6
4-7
n.a.
4.5
IEP
+-sense ss RNA
–sense ssRNA
+-sense ssRNA
ds RNA
+-sense ss RNA
+-sense ss RNA
ds RNA
+-sense ss RNA
+-sense ss RNA
+-sense ss RNA
linear ds DNA
Nucleic Acids
door handles, phones
table, bedding,
remote control
toys, counters,
keyboards, toilets
desks, computers,
phones, tables,
lightswitch
-
table, bedding,
glasses, lamp
phone, toilet,
light switch
phone, toilet
bowl
carpets, phones
lightswitch, toilet
phone, lightswitch,
doorknob,
toys, tables
Detected on Fomites
0.21.25
2
0.0830.33
0.751.5
0.25
0.0028
n.a.
0.0010.002
0.0028
0.0028
>25
2.5
2-6
24-48
3-12
>1440
360>720
n.a.
1440
>720
n.a.
0.63-0.95
0.0280.042
0.5
1
0.006
-0.33
0.0028
0.002
-0.025
0.0028
0.011
n.a.
5-8
10
72
2-12
72>720
1601350
>1440
160
360
Persistence on Surfaces
Porous
Non-Porous
Rate
time(h) Rate
time(h)
0.51%
proven
0-1.5%
n.a.
n.a.
1-16%
7-13%
proven
n.a.
n.a.
Transfer
(hand-fomite)
Table 1.2: Common gastrointestinal and respiratory viruses transmitted via fomites, including chemicophysical
attributes, detection on fomites, inactivation rates on surfaces, and evidence of transfer between hands and fomites.
Nt
“IEP” is the isoelectric point of the virus. Inactivation rate is in units (-log10 ( N
)) as measured in days. “RSV”
o
is respiratory syncytial virus. Enterovirus persistence and transfer rates were estimated using porcine enterovirus.
Norovirus persistence and transfer was estimated using feline calicivirus. “n.a.” is used where data are not available.
Data adapted and compiled from Hall et al. (1980); Ansari et al. (1988, 1991); Abad et al. (1994); Long et al. (1997);
Boone and Gerba (2007, 2010); Michen and Graule (2010) and Chapter 3
80-120
60-80
Rotavirus
Influenza
27-38
Norovirus
80-220
17-28
Enterovirus
Respiratory
Coronavirus
28-35
90-100
Size (nm)
Astrovirus
Gastrointestinal
Adenovirus
Virus
CHAPTER 1. INTRODUCTION
18
CHAPTER 1. INTRODUCTION
1.8
Figures
19
CHAPTER 1. INTRODUCTION
20
Infectious Disease Transmission Routes
Vectorborne
Airborne
n
atio
pir
ex
vector
susceptible host
susceptible host
susceptible host
infected host
infected host
Pathogens: malaria, yellow fever, dengue
African trypansimiasis
Interventions: insecticides, environmental
mitigation, bed nets, window screens,
insect repellents
Common Vehicle
Pathogens: influenza, measles, rhinovirus,
respiratory syncytial virus
Interventions: respiration masks, social distancing,
closing public locations, blocking expirations,
mechanical filtration, ultraviolet radiation.
Contact
Foodborne
Direct
contaminated
foodstuffs
susceptible host
infected host
Waterborne
susceptible host
Indirect
fomite
drinking
water
bathing
water
recreational
water
Iatrogenic
contaminated medical
device, blood, or tissue
infected host
susceptible host
Vertical
susceptible host
Pathogens: norovirus, enterovirus, rotavirus,
poliovirus, rhinovirus, hepatitis A
Interventions: water and food quality standards,
hand and environmental hygiene, donor
blood and organ screening, equipment
sterilization
infected host
(mother)
susceptible host
(prenatal child)
Pathogens: rotavirus, rhinovirus, norovirus
enterovirus, hepatitis, human immunoviruss,
Interventions: hand and environmental hygiene,
pharmaceuticals, prophalxysis.
Figure 1.1: Infectious disease transmission routes as grouped into four common categories with examples of common interventions used to reduce burden from example
pathogens. Arrows represent movement or transfer of pathogen
d to hand
she
mite
persist on fo
2
tion
4
susceptible host
e infec
initiat
trans
fer fomit
e to hand
3
mouth
ite to
m
o
f
er
nsf
tra
transfer
hand to
mouth
Figure 1.2: For fomites to act as intermediaries in infectious disease, an etiological agent must be capable of
following four distinct steps. The first step (“1”) is that an infectious agent most be shed from an infected host
to the fomite. Two common pathways are direct shedding (e.g., large droplet setting from coughing, sneezing, or
other expiration) or indirect shedding via hands (e.g., inadequate hygiene after using a restroom facility followed
by handling a doorknob). The second step (“2”) is that an infectious agent must be able to persist on a fomite
for a period sufficient for the fomite to come into contact with a susceptible host. The third step (“3”) is that
an infectious agent must transfer from the fomite to a susceptible host, either by direct fomite-mouth contact
(e.g., a child mouthing a toy) or by indirect fomite-hand contact (e.g., handling a colleagues cellphone) followed by
hand-mucuos membrane contact (the average adult toches his lips or mouth 10-25 times per hour)
.
infected host
fer
trans o fomite
t
d
n
a
h
1
shed to fo
mit
e
Fomite-Mediated Transmission
CHAPTER 1. INTRODUCTION
21
Chapter 2
Virus transfer between fingerpads
and fomites
The results presented in this chapter originally appeared as a research article in
the December 2010 volume of the Journal of Applied Microbiology (Julian et al.,
2010). James O. Leckie and Alexandria B. Boehm appear as co-authors, for their
contributions to study design, data interpretation, and manuscript improvement.
22
CHAPTER 2. VIRUS TRANSFER
2.1
23
Abstract
Aims Virus transfer between individuals and fomites is an important route of transmission for both gastrointestinal and respiratory illness. The present study examines
how direction of transfer, virus species, time since last handwashing, gender, and titer
affect viral transfer between fingerpads and glass.
Methods and Results Six hundred fifty-six total transfer events, performed
by twenty volunteers using MS2, φX174, and fr indicated 0.23 ± 0.22 (mean and
standard deviation) of virus is readily transferred on contact. Virus transfer is significantly influenced by virus species and time since last handwashing. Transfer of
fr bacteriophage is significantly higher than both MS2 and φX174. Virus transfer
between surfaces is reduced for recently washed hands.
Conclusions Viruses are readily transferred between skin and surfaces on contact. The fraction of virus transferred is dependent on multiple factors including
virus species, recently washing hands, and direction of transfer likely due to surface
physicochemical interactions.
Significance and Impact of Study The study is the first to provide a large data
set of virus transfer events describing the central tendency and distribution of fraction
virus transferred between fingers and glass. The data set from the study, along with
the quantified effect sizes of the factors explored, inform studies examining role of
fomites in disease transmission.
Keywords Virus transfer, surfaces, fomites, hand hygiene, environmental hygiene, quantitative microbial risk assessment, bacteriophage.
CHAPTER 2. VIRUS TRANSFER
2.2
24
Introduction
To better understand transmission routes for viral disease and develop more refined
quantitative microbial risk assessment models (Atkinson and Wein, 2008; Nicas and
Jones, 2009; Julian et al., 2009) additional information on the importance of fomites
in the transmission of viruses is needed (Boone and Gerba, 2007; Brankston et al.,
2007). Insight into the role of fomites in the transmission of infectious disease can be
obtained by studying the transfer of viruses between skin and surfaces.
Virus transfer between skin and surfaces can be described quantitatively by the
fraction of virus on a contaminated (donor) surface that is transferred on contact to
a recipient surface (Reed, 1975; Gwaltney, 1982; Ansari et al., 1988; Mbithi et al.,
1992; Rusin et al., 2002). This fraction could be modulated by a number of factors
including the donor / recipient surfaces and the virion surface.
Previous studies have reported a wide range of transfer fractions (0.0001 to 0.67)
for transfer of a single bacteriophage (e.g., PRD-1) or pathogenic virus (e.g., rotavirus,
hepatitis A, human parainfluenza virus-3, rhinovirus) between skin and various surfaces (Reed, 1975; Ansari et al., 1988; Mbithi et al., 1992; Rusin et al., 2002; Bidawid
et al., 2004). The range of transfer fractions is significantly influenced by the type of
surface (porous or non-porous) contacted by the skin, with transfer between porous
and food (e.g., cloth, lettuce, ham, beef, and carrots (Rusin et al., 2002; Bidawid
et al., 2004)) surfaces generally lower than transfer to non-porous (e.g., stainless steel
and plastic (Reed, 1975; Rusin et al., 2002; Bidawid et al., 2004)) surfaces.
Only one published study has examined the transfer between skin and surface
of more than one virus. In particular, Ansari et al. (1991) reported a difference in
fraction transferred for rhinovirus and human parainfluenza virus-3 between fingers
and metal disks. However, the small sample size of the study presumably precluded
statistical analysis.
The present study explores how viral species and factors including inoculum size,
direction of transfer, and skin condition affects virus transfer. We quantify the transfer
of three different viruses, MS2, fr, and φX174, between fingerpads and a glass surface.
Additionally, we applied experimental treatments to isolate the effects of the following
CHAPTER 2. VIRUS TRANSFER
25
on virus transfer: (1) inoculum size, (2) direction of transfer, and (3) skin condition
defined by the gender and time since last hand washing. Inoculum size may influence
fraction of virus transferred as the phenomenon was demonstrated in bacterial transfer
by Montville and Schaffner (2003). Direction of transfer refers to the direction that
virus is transferred, such as from skin-to-fomite versus from fomite-to-skin. Gender
may influence virus transfer because men typically have a significantly lower skin pH
(van de Vijver et al., 2003). Similarly, hand washing shifts the biological and chemical
characteristics of the skin by decreasing organic and inorganic constituents (e.g.,
sebum, sweat, microflora), increasing pH, and decreasing hydrophobicity (Elkhyat
et al., 2001; Kownatzki, 2003; Barel et al., 2009). To our knowledge, this is the first
study to examine the effects of virus species, inoculum size, and skin condition on
virus transfer between skin and a surface.
2.3
2.3.1
Materials and Methods
Volunteers
Permission of the Stanford University Research Compliance Office for Human Subjects Research was obtained prior to the study. Volunteers included 8 males and 12
females, with an age range of 20-32 years. To standardize unwashed state of volunteers’ hands, volunteers washed their hands for 15 seconds using soap and water at
least 1.5 hours before the experiment, and avoided eating or going to the restroom
within that time frame. No brand or type of soap was recommended or provided, and
no effort was made to account for residual effects of soap products used before the
experiment.
2.3.2
Virus and Preparation of Inoculum
This study quantifies transfer of three different bacteriophage (MS2, fr, and φX174)
obtained from the American Type Culture Collection (ATCC). MS2 (ATCC #15597B1), fr (ATCC #15767-B1), and φX174 (ATCC #13706-B1) strains were chosen
because they have similar size (19-27 nm) and shape (icosahedral, no tail) to several
CHAPTER 2. VIRUS TRANSFER
26
human viruses, such as norovirus (Abbaszadegan et al., 2007). MS2 and fr bacteriophage are both +-sense RNA viruses of the Leviviridae family, with similar surface
characteristics but different isoelectric points (3.9 and 8.9, respectively) (Gerba, 1984;
Liljas et al., 1994; Dowd et al., 1998; Herath et al., 1999). φX174 is a single stranded
DNA virus of the Microviridae family with an isoelectric point of 6.6 (Gerba, 1984;
Dowd et al., 1998).
The inoculum used in the study was prepared by propagating the model viruses
to a concentration of 108 -1010 plaque forming units (PFU)/ml in phage buffer (Reddy
et al., 2006). The propagated virus was then enumerated and diluted to approximately
105 to 106 PFU/ml using tryptic soy broth (TSB, pH of 7.2 ± 0.2) to be used as virus
stock. TSB is an organic-rich media intended to act as a model for the broad range of
matrices in which respiratory and gastrointestinal viruses contaminate fomites (e.g.
vomitus, urine, feces, mucus, and saliva). Use of homogeneous and well-characterized
TSB was intended to reduce variability introduced by use of natural media such as
fecal suspensions, mucus, or saliva. The virus stock was enumerated during every
experiment to confirm titer.
2.3.3
Plaque Assay
The double agar layer procedure was used to enumerate virus (USEPA, 2001). The
hosts were Escherichia coli K12-3300 (ATCC #19853) for fr, E. coli HS(pFamp)R
(ATCC #700891) for MS2, and E. coli CN-13 (ATCC #700609) for φX174. The
double agar layer procedure was chosen to estimate the fraction of infective virus
transferred on contact.
2.3.4
Virus Transfer
To determine the amount of virus transferred on contact between a fingerpad and
a nonporous glass surface, we used a protocol adapted from Ansari et al. (1991).
Specifically, we inoculated either between 100 and 600 or between 1000 and 6000
PFU diluted in TSB on the donor surface in an aliquot of 5 µl to represent low
and high titers, respectively. Borosilicate coverslips are uniform, smooth, and clean
CHAPTER 2. VIRUS TRANSFER
27
surfaces providing a proxy for non-porous fomites with consistent characteristics. All
surfaces, including the fingerpads, were subsequently allowed to visibly dry while
supervised by the technician before contact between surfaces was made to mimic
drying after natural contamination events. To verify that the inoculate remained on
the fingerpads, the volunteer was supervised during the visible drying. All samples
from which no virus could be recovered from either the donor or recipient surface
following the transfer event were removed from analysis.
The volunteer placed the donor and recipient surfaces in contact for 10 ± 1 s
with an average constant pressure of 25 kPa (range of 16 kPa-38 kPa) controlled by
counterbalancing a triple beam balance weighted to 500 g. 25 kPa is comparable to the
pressure exerted by a child while gripping an object, the pressure exerted locally on
the fingerpads for adults using handtools, and the pressure used in studies examining
transfer of soil from surfaces to skin (Link et al., 1995; Hall, 1997; Ferguson et al.,
2009). We used a cotton-tipped swab applicator, wet in 500 µl of phosphate buffer
saline (PBS, 1 mM potassium phosphate monobasic, 155 mM sodium chloride, and 3
mM sodium phosphate dibasic, pH of 7.4 ± 0.05, from Invitrogen, Carlsbad, CA), to
remove virus from the surfaces. The applicator was wiped firmly against the surface
in a sweeping, rotating, motion for 10 s before being placed back into the remaining
PBS and vortexed for 10 seconds. We used separate swabs to remove virus from the
donor and recipient surfaces. Samples were aliquoted into 100 µl of 100 , 10−1 , and
10−2 dilutions in PBS; the dilutions were assayed using the double agar layer method
(USEPA, 2001). The range of detection for this method is 10 PFU to 200000 PFU.
If virus was unrecoverable from a surface, the lower detection limit of 10 PFU was
used as an estimate for the virus recovered. The fraction transferred (f ) is defined
as PFU recovered from the recipient surface (RR ), relative to PFU recovered from
the sum of the donor (RD ) and the recipient surfaces, as previously described (Rusin
et al., 2002):
f=
RR
(RR + RD )
(2.1)
Dessication, or the drying of the inoculum on the surface, results in a loss of virus
titer (Ansari et al., 1988, 1991; Rusin et al., 2002). Because the surface is dried prior
CHAPTER 2. VIRUS TRANSFER
28
to the transfer event, the seeded inoculum is higher than the sum of virus recovered
from donor and recipient surfaces after the transfer event. We chose to calculate f
using just recoverable virus from the donor and recipient surfaces (Equation 2.1) so
that the virus inactivated by dessication is not included in the fraction of virus transferred estimated by Equation 2.1. We assume that the relatively short time of the
contact event and subsequent hand and surface sampling does not contribute to loss in
titer due to inactivation. The experimental design varied factors including low/high
titer and direction of transfer, with blanks and replicates across the 10 fingerpads.
Four randomly chosen fingerpads were assigned the following four titer/direction-oftransfer factor combinations: (1) low titer/glass-to-fingerpad, (2) low titer/fingerpadto-glass, (3) high titer/glass-to-fingerpad, and (4) high titer/fingerpad-to-glass. Four
additional fingerpads were assigned the same factor combinations. As all factor levels
of the fingerpads of the first set were identical to the factor levels of the fingerpads
on the second set, the second set of contact events are defined as replicates for the
contact events from the first set. In this manner, every contact event had a corresponding replicate contact event. The remaining two fingerpads (one on each hand)
were selected to act as blanks. A blank is defined as a transfer event where fingerpad
or glass was inoculated with TSB that did not contain any virus. After the initial
10 transfer events were completed, the volunteers washed their hands for 15 s usc antibacterial liquid hand soap (Colgate-Palmolive, New York, NY),
ing Softsoap
c scientific cleaning wipe (Kimberlyrinsed in tap water, and dried with a Kleenex
Clark, Irving, TX) under the technician’s instruction. We then used the same factor
assignments for each fingerpad to measure transfer for the ’washed’ hands. Twenty
volunteers performed the experiment using MS2 bacteriophage, thirteen of the twenty
volunteers repeated the experiment using φX174 bacteriophage, and ten of the thirteen repeated a third time using fr bacteriophage. Ten volunteers completed all 3
experiments. Temperature and relative humidity were recorded from a thermometer
and hygrometer (Springfield Precision Instruments, Wood Ridge, NJ) kept at the
sampling location.
CHAPTER 2. VIRUS TRANSFER
2.3.5
29
Statistics
All statistics were performed using the R statistical software package (R: A Language
for Statistical Computing, version 2.9.0, R Foundation for Statistical Computing, Vienna, Austria). Where appropriate, descriptive statistics (mean, median, and standard deviation) are reported. Statistical significance was assessed using a significance
level of α = 0.05. The significance of experimental factors (direction of transfer,
gender, virus species, time since last handwash, and titer) on percent of virus transferred was assessed using n-way ANOVA on untransformed data. Tukey’s post-hoc
test assessed significant differences between the transfer of each phage type. Distribution parameters for normal, lognormal, and Weibull distributions are reported for
the data on fraction virus transferred between surfaces (f ) stratified by phage type.
These distributions are used to describe microbial and/or chemical transfer (Chen
et al., 2001; Beamer, 2007; Pẽrez Rodrı́guez et al., 2007). Five-fold cross-validation
and Kolmogov-Smirnoff methods were used to determine distribution parameters and
goodness-of-fit.
2.4
2.4.1
Results
Virus Transfer
f was quantified for 656 transfer events. Eleven transfer events (<2% of total transfers) of the original 688 were excluded because of a laboratory error (e.g. mislabeling
and failure to add host) involving at least one of the two samples (donor or receipient surface). An additional twenty-one transfer events (<3% of total transfers)
were excluded because virus could not be recovered from both donor and recipient
surfaces after the transfer. All blanks were negative, implying fingerpads were not
contaminated prior to study and no cross-contamination occurred during inoculation.
Aggregating data for all three virus species, ranged from 0.001 to >0.999 with a median, mean, and standard deviation of 0.18, 0.23, and 0.22, respectively. Median,
mean, and standard deviation of f were 0.32, 0.31, and 0.20, respectively, for fr; 0.18,
0.23, 0.21, respectively, for MS2; and 0.09, 0.19, 0.24 for φX174.
CHAPTER 2. VIRUS TRANSFER
30
An n-way ANOVA investigated treatment effects on f . Gender (p = 0.42) and titer
(p = 0.79) were not significant. Direction of transfer (p = 0.01) and time since last
hand wash (p = 0.002) were significant, with glass-to-fingerpad and unwashed hands
transferring a greater fraction than fingerpad-to-glass and washed hands, respectively.
Additionally, virus species was significant (p < 0.001). f was larger for fr than for
both MS2 (Tukey’s test p < 0.001) and φX174 (p < 0.001). f was not significantly
different between MS2 and φX174 (p = 0.16). The mean, median, and standard
deviation of f are presented in Table 2.1 grouped by significant factors (e.g., glassto-washed finger transfer of MS2 bacteriophage, unwashed finger-to-glass transfer of
fr bacteriophage, etc).
Parameters describing the distribution of f were determined for normal, lognormal, and Weibull distributions and are available, with estimates of goodness-of-fit,
separated by virus species, in Table 2.2. Virus species impacts not only mean f , but
also the best-fit distribution; MS2 and φX174 are right-skew while fr bacteriophage
has a more left-skew distribution. As evidence, histograms of the data with corresponding best fit probability density functions are provided in Figure 2.1, separated
by virus species and direction of transfer.
Temperature and relative humidity ranged from 20-22◦ C and 45-60%, respectively,
over the course of the study. No statistically significant correlation (using Spearman’s
correlation coefficient) between temperature and f was found for fr (ρs = 0.06, p =
0.47), MS2 (ρs = 0.05, p = 0.65), or φX174 (ρs = −0.02, p = 0.75) or between relative
humidity and f for fr (ρs = 0.08, p = 0.31), MS2 (ρs = −0.06, p = 0.57), or φX174
(ρs = 0.04, p = 0.59).
2.5
Discussion
We demonstrate that viruses are readily transferred between skin and a model fomite
surface. Aggregating 656 viral transfer events, the mean fraction of virus transferred,
f , is 0.23 ± 0.22 (mean and standard deviation), consistent with previous studies
on virus transfer (Ansari et al., 1991; Mbithi et al., 1992; Rusin et al., 2002) and
may be applicable as transfer estimate for viruses of similar size and shape, such as
CHAPTER 2. VIRUS TRANSFER
31
norovirus. The relatively large sample sizes of volunteers and contact events provide
robust data to estimate distributions to describe f , an important parameter needed
for quantifying microbial risk (Gibson et al., 1999; Nicas and Sun, 2006; Wein and
Atkinson, 2009), especially in models that utilize activity data (Julian et al., 2009). f
is influenced by the virus species, the direction the virus is transferred (i.e., fingerpadto-surface or surface-to-fingerpad), and the characteristics of an individual’s skin, in
particular whether or not the hands have recently been washed. Although statistically
significant, the factors we identified as influential may change the fraction of virus
transferred by, at most, only 5-10%. This is small relative to the effect of changing
the porosity of the fomite surface which has been shown to shift f by as much as 2
orders of magnitude (Scott and Bloomfield, 1990; Rusin et al., 2002). Although the
contribution of fomites relative to other transmission routes in perpetuating disease
burden remains uncertain, the present study suggests it is specific to the etiological
agent and ameliorated through frequent hand washing.
Virus species affects both the mean and distribution of f . Our work expands
on the work of Ansari et al. (1991) who observed transfer differences between two
human viruses using 18 total transfer events, by measuring over 600 transfer events
with three different viruses. Our high number of observed transfers allowed rigorous
statistical testing of treatments. Our results also demonstrate that f is influenced
by the interaction of virus species and direction of transfer (Table 2.1). In other
words, f depends on the direction of transfer, but precisely how well depends on
viral species. This is consistent with observations described in the literature. Ansari
et al. (1991) demonstrated human parainfluenza type 3 virus transfer is greater from
fomite-to-fingers than fingers-to-fomite, while Mbithi et al. (1992), using hepatitis A
virus, demonstrated the reverse: greater transfer from fingers-to-fomite than fomiteto-fingers.
Washing fingerpads prior to a virus transfer event reduces f . The reduction in
virus transfer due to washing is greater for fingerpad-to-glass transfer than glassto-fingerpad transfer. Changes in moisture level and pH on skin from handwashing
(Gfatter et al., 1997), or other residual effects from the soap may contribute to this
effect. To investigate the causal mechanism of reductions in f due to hand washing,
CHAPTER 2. VIRUS TRANSFER
32
future studies could incorporate moisture and pH measurements of the volunteers’
fingerpads.
The impact of hand washing with soap and water on reduction of gastrointestinal and respiratory illness is well documented (Aiello et al., 2008), and is generally
attributed to the reduction of pathogenic bacteria and virus on the hands (Curtis
et al., 2000; Pickering et al., 2010). The results suggest that reduced viral transfer during hand-surface contacts could also contribute to illness reduction. Further
study of virus transmission may elucidate whether or not this finding extends to field
conditions.
The influence of virus species on f could be due to the physicochemical properties of the virus. The surfaces, suspension media, and contact mechanics were kept
constant throughout the study, and the experiments were carried out in ambient
laboratory conditions such that temperature and humidity varied over small, but realistic, ranges. Because the viruses were the same shape (icosahedral), we attribute
the observed differences in f between virus species to the different sizes (19-27 nm)
and chemical properties of the virus capsids. In this experiment, the bacteriophage
studied (MS2, φX174, and fr) have different net surface charge, as evidenced by the
different isoelectric points (3.9, 6.6, and 8.9, respectively) (Dowd et al., 1998; Herath
et al., 1999) and different hydrophobicities. Specifically, φX174 was identified as the
most hydrophilic and MS2 as the most hydrophobic in a study of 13 virus species
by Shields and Farrah (2002); fr was not tested. Further research in this area is
warranted.
Neither gender, inoculum size, temperature, nor humidity significantly influenced
f . Significant differences in skin characteristics due to gender, such as pH, have
previously been documented but the differences are small (pH of male skin was 4.7,
female skin was 5.0) (van de Vijver et al., 2003). This difference in pH was not
large enough to affect viral transfer in the present study. Inoculum size also did
not significantly influence f , in contrast to previous work with bacteria that showed
inoculum size significantly influenced bacterial f over multiple orders of magnitude
(Montville and Schaffner, 2003). Perhaps the range of titer we explored (one order of
magnitude) was too low to observe an effect. Similarly, as neither temperature nor
CHAPTER 2. VIRUS TRANSFER
33
relative humidity were explicitly investigated in this study, the range in temperature
(20-22◦ C) and relative humidity (45-60%) may have been too small to observe an
effect on f .
There are several limitations to our study design. We minimized inter-trial variability by using glass surfaces, controlling for duration and pressure of contact, and
using the same group of volunteers. In field conditions, such as when an individual
contacts a virus-contaminated surface, variation may be greater as transfer events occur between a wide range of surfaces over a range of durations and contact pressures.
The use of an infectivity assay (the double agar layer method) does not provide information on non-infective virus particles transferred on contact. Similarly, one plaque
forming unit may be more than one infective viral particles (Galasso and Sharp, 1962).
Accounting for the presence of non-infective virus particles or multiple infective virus
particles in one plaque may alter the fraction of infective virus transferred. Future
studies could incorporate molecular methods to better understand transfer influence
of non-infective particles and multiple virus per plaque forming unit on transfer.
2.6
Acknowledgments
This work was supported, in part, by the Shah Research Fellowship of Stanford
University and by the United States Environmental Protection Agency (EPA) under
the Science to Achieve Results (STAR) Graduate Fellowship Program. EPA has not
officially endorsed this publication and the views expressed herein may not reflect the
views of the EPA. The authors acknowledge the volunteers who participated in this
study. Additionally, the authors thank the Boehm Lab, Robert Canales, Francisco
Tamayo, and the anonymous reviewers who assisted with the work and/or provided
suggestions for improving the manuscript.
CHAPTER 2. VIRUS TRANSFER
2.7
Tables
34
CHAPTER 2. VIRUS TRANSFER
Phage
MS2
Direction
Handwash n
Finger-to-glass Unwashed 75
Washed
75
Glass-to-finger Unwashed 75
Washed
80
φX174 Finger-to-glass Unwashed 49
Washed
50
Glass-to-finger Unwashed 48
Washed
47
fr
Finger-to-glass Unwashed 36
Washed
40
Glass-to-finger Unwashed 40
Washed
40
35
µ̂
median
σ̂
0.24
0.18
0.24
0.15
0.10
0.16
0.25
0.19
0.23
0.26
0.21
0.19
0.26
0.16
0.28
0.17
0.14
0.17
0.21
0.07
0.29
0.11
0.04
0.18
0.28
0.25
0.21
0.20
0.19
0.16
0.37
0.39
0.22
0.39
0.40
0.11
Table 2.1: The number of trials (n), mean (µ̂), median, and standard deviation (σ̂)
of f for data subset by factors determined to be significant via n-way ANOVA (virus
species, direction of transfer, and skin condition as determined by time since last
handwash)
CHAPTER 2. VIRUS TRANSFER
36
Normal
Phage Type
MS2
φX174
fr
All Phage
µ̂
σ̂
0.23 0.22
0.19 0.24
0.31 0.20
0.23 0.22
p-value
0.09
0.03
0.45
<0.01
Lognormal
µ̂
σ̂
-2.1 1.4
-2.6 1.5
-1.6 1.1
-2.1 1.4
Weibull
p-value
shape
0.18
0.43
0.14
<0.01
0.96
0.77
1.4
0.94
scale p-value
0.22
0.16
0.34
0.23
0.12
0.84
0.66
0.09
Table 2.2: The parameters (mean (µ̂), standard deviation (σ̂), shape, and scale) and
goodness-of-fit for fitting normal, lognormal, and Weibull distributions to the fraction of virus transferred as determined by 5-fold cross validation. Parameters and
goodness-of-fit are determined for each bacteriophage individually, and all bacteriophage aggregated
CHAPTER 2. VIRUS TRANSFER
2.8
Figures
37
CHAPTER 2. VIRUS TRANSFER
Glass-toFingerpad
fr
MS2
(a)
(b)
n= 99
n= 150
(e)
(c)
n= 76
(f)
n= 95
0.0 0.2 0.4 0.6 0.8 1.0
ALL
n= 155
0.0 0.2 0.4 0.6 0.8 1.0
Normal
(d)
n= 325
(g)
n= 80
0.0 0.2 0.4 0.6 0.8 1.0
5
4
3
2
1
(h) 5
n= 330
Density
Fingerpad
-to-Glass
φX174
38
4
3
2
1
0.0 0.2 0.4 0.6 0.8 1.0
Fraction Transferred
Weibull
Lognormal
Figure 2.1: Histogram of f for (a) φX174 fingerpad-to-glass, (b) MS2 fingerpad-toglass, (c) fr fingerpad-to-glass, (d) all bacteriophage fingerpad-to-glass, (e) φX174
glass-to-fingerpad, (f) MS2 glass-to-fingerpad, (g) fr glass-to-fingerpad, and (h) all
bacteriophage glass-to-fingerpad. The probability density function is overlaid on each
histogram using the parameters reported in Table 2.2
Chapter 3
A Model of Exposure to Rotavirus
from Nondietary Ingestion Iterated
by Simulated Intermittent
Contacts
The results presented in this chapter originally appeared as a research article in
the May 2009 issue of the journal Risk Analysis (Julian et al., 2009). Robert A.
Canales contributed extensively to the modeling and statistical analysis presented
and is a co-author on the publication. James O. Leckie and Alexandria B. Boehm
also appear as co-authors, for their contributions to study design, data interpretation,
and manuscript improvements.
39
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
3.1
40
Abstract
Existing microbial risk assessment models rarely incorporate detailed descriptions
of human interaction with fomites. We develop a stochastic-mechanistic model of
exposure to rotavirus from nondietary ingestion iterated by simulated intermittent
fomes-mouth, hand-mouth, and hand-fomes contacts typical of a child under six years
of age. This exposure is subsequently translated to risk using a simple static doseresponse relationship. Through laboratory experiments, we quantified the mean rate
of inactivation for MS2 phage on glass (0.0052/s) and mean transfer between fingertips and glass (36%). Simulations using these parameters demonstrated that a childs
median ingested dose from a rotavirus-contaminated ball ranges from 2 to 1,000 virus
over a period of one hour, with a median value of 42 virus. These results were heavily
influenced by selected values of model parameters, most notably, the concentration
of rotavirus on fomes, frequency of fomes-mouth contacts, frequency of hand-mouth
contacts, and virus transferred from fomes to mouth. The model demonstrated that
mouthing of fomes is the primary exposure route, with hand mouthing contributions
accounting for less than one-fifth of the childs dose over the first 10 minutes of interaction.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
3.2
41
Introduction
Viral agents transmitted primarily via the fecal-oral route, including enteric adenovirus, astrovirus, norovirus, and rotavirus, are responsible for 35% of hospitalizations
for gastroenteritis, accounting for almost 5% of all child hospital visits in the United
States (Malek et al., 2006). Enteric viruses have been detected on indoor surfaces and
fomites in hospitals, day care centers, hotels, and houseboats (Keswick et al., 1983;
Green et al., 1998; Cheesbrough et al., 2000; Jones et al., 2007). Evidence of the
role of fomites in disease transmission includes the ability of the etiological agents to
transfer between hands and fomes (Ansari et al., 1988) and between fomes and mouth
(Rusin et al., 2002), and their ability to persist on fomes and hands (Hall et al., 1980;
Casewell and Desai, 1983; Ekanem et al., 1983; Butz et al., 1993; Abad et al., 1994;
Cheesbrough et al., 1997; Das et al., 2002; Clay et al., 2006). Despite evidence of
the importance of fomites in the spread of disease, few quantitative microbial risk
assessment models have examined their role in transmission of disease (Gibson et al.,
1999; Nicas and Sun, 2006).
The sporadic and sequential nature of multiple individual contacts between hands
and fomites, hands and mouth, and fomites and mouth has generally not been considered in microbial exposure assessments. Instead, human interaction with fomites
is modeled using estimates of the probability that a contact event occurs (e.g., 10%
chance a fomes is contacted by hand), a constant frequency of the contact event (e.g.,
mouth is contacted by hand 0.08 times per minute), or a constrained sequence of
events (e.g., fomes touches hand, hand then touches mouth) (Gibson et al., 1999;
Chen et al., 2001; Gibson et al., 2002; Haas et al., 2005; Nicas and Sun, 2006; Nicas
and Best, 2008). In the latter, frequently used in quantitative risk assessment as it
pertains to food handling, researchers assume that contacts are inevitable, only one
contact event of each type occurs, and the temporal sequence is static. In the present
study, we further the understanding of the role of human temporal sequence is static.
In the present study, we further the understanding of the role of human interaction
with fomites on exposure to infectious agents by incorporating modeled sequential,
intermittent contacts to encompass a wide array of activity levels. Previous work
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
42
in chemical risk modeling has suggested that using modeled sequential, intermittent
contact events reduces uncertainty in estimates of exposure to fomites (Ferguson,
2003).
Chemical risk models have been developed that account for an individuals sequential contacts with fomites using micro-level activity data. These data are obtained
typically through the use of videotapes by transcribing the timing and sequence of
an individuals interactions with environmental surfaces that can act as fomites (Ferguson et al., 2006). With these data in hand, along with concentrations of chemical
residues on objects and knowledge of the ability of chemicals to transfer from objects
to hands and mouth, a modeler is able to estimate chemical dose (Zartarian et al.,
1995; Ferguson, 2003; Ferguson et al., 2006).
The present study draws on this chemical risk model framework to conduct a
microbial risk assessment of a childs interaction with a rotavirus contaminated ball
in an indoor environment (e.g., a child care center). Using micro-level activity data
allows us to examine the influence of sequential contact events on a childs exposure
to rotavirus and subsequently estimate the risk of infection. After experimentally
determining inactivation rates of virus on a surface and the transfer efficiencies of virus
between a surface and human hands, we formulate a stochastic-mechanistic model of
risk from nondietary ingestion of rotavirus resulting from fomes-mouth, hand-mouth,
and hand-fomes contacts. The model is novel in that it uses variable and sequential
microlevel activity data to quantify exposure to a contaminated fomes. One of the
overarching goals of this study is to determine which model parameters need to be
further studied so that more precise exposure assessments can be performed.
3.3
Model Description
The stochastic-mechanistic model was developed with MATLAB (version 7.0; The
Mathworks, Inc., Natick, MA, USA). The model estimates an individual’s viral dose
over the specified time period by incorporating both direct contact between the mouth
and a contaminated fomes and indirect contact between the mouth and the fomes via
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
43
intermittent hand-fomes/hand-mouth contacts. Discrete, mechanistic equations iterated by contact event relate an individual’s micro-level activity data (e.g., fomes-hand,
fomes-mouth, and hand-mouth contact frequency) to virus-specific exposure factors
(e.g., surface area contacted, hand-fomes viral transfer efficiency) and dose-response
parameters to estimate exposure, dose, and risk of adverse health outcomes. A Monte
Carlo sampling generates model parameter inputs from defined probability distributions, allowing the incorporation of parameter uncertainty. Parameter uncertainty is
defined here to include both uncertainty and variability. The model output includes
temporal concentration profiles for the fomes, left hand, and right hand and characterizes an individual’s cumulative and iterative risk from continued interaction with
the fomes.
Equations used in the model define an infectious virus (hereafter referred to as
“virus”) as being in one of five states, as depicted in Figure 3.1: (1) located on the
fomes, (2) located on the right hand, (3) located on the left hand, (4) irreversibly
inactivated, and (5) absorbed in the facial membrane as dose. At the start of each
model simulation, the fomes is contaminated with a uniform surface concentration of
virus. Additionally, the individual’s hands and mouth are assumed free of virus. The
movement of virus between the states occurs either through inactivation (states 1 →
4, 2 → 4, and 3 → 4) or through transfer of virus via contact (states 1 ↔ 2, 1 ↔ 3,
1 → 5, 2 → 5, 3 → 5).
Viral inactivation is assumed to decay exponentially with time, causing virus to
move from states 1, 2, and 3 to state 4:
Cx (tx ) = Cx0 e(−kx tx )
(3.1)
where Cx (tx ) with units virus/cm2 is the concentration of virus on surface x (e.g.,
fomes or hand) at time t, Cx0 is the initial concentration of virus on the surface
(virus/cm2 ), kx is the inactivation rate of the virus on the surface (s−1 ), and tx is the
elapsed time(s).
The transfer of virus between surfaces upon contact is modeled by assuming that
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
44
transfer is driven by a concentration gradient:
CX = CX0 − AXY T EXY (CX0 − CY 0 )
(3.2)
where CX is the concentration of virus on surface X after contact with surface
Y (virus/cm2 ), CX0 and CY 0 are the virus concentrations on surfaces X and Y ,
respectively, prior to contact (virus/cm2 ), T EXY is the percentage of virus transferred
from surface X to surface Y (%), and AXY is the ratio of surface area of the contact
event between surfaces X and Y to the total surface area of surface X (cm2 /cm2 ).
The transfer is assumed to occur instantaneously and uniformly, and the duration
of contact is assumed to not affect transfer. The latter is based on the work of
Cohen Hubal et al. (2008), who found that duration does not increase the amount of
both lipophilic uvitex and nonlipophilic riboflavin tracer residues transferred between
surfaces on contact (Cohen Hubal et al., 2008). It is assumed that, after transfer,
virus is distributed evenly over the entire surface.
Dose (D) is the number of virus that transfer from a surface to the mouth and
depends on contact area between the surface and the mouth, as follows:
D = Sx T Exf Cx
(3.3)
where Sx is the contact area between object x and the mouth (cm2 ), T Exf is
the percentage of virus transferred from the object to the mouth, and Cx is the
concentration of virus on object x (virus/cm2 ). The mouth is assumed to be an
absorbing state, as previously described (Nicas and Sun, 2006) so virus in contact
with the mouth is instantly absorbed into the body.
A dose-response curve for rotavirus is used to determine the likelihood of adverse
health outcome (Haas et al., 1999; Teunis et al., 1999). Because our model results
in multiple ingested doses from subsequent fomes-mouth and hand-mouth contacts,
we assume that the likelihood of an adverse health outcome is determined from the
additive effect of multiple subsequent exposures, as follows (Haas et al., 1999):
RT OT = f

I
X

Di
i=1
+
J
X
j=1
Dj +
K
X
k=1

Dk 
(3.4)
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
f (D) = 1 − 1 +
45
−α
D 1
(2 α − 1)
N50
(3.5)
Here, RT OT is the likelihood of adverse health outcome(%), f is the dose-response
function (Equation 3.5), which is unique for rotavirus (Haas et al., 1999). I, J, and
K, are the total number of fomes-mouth, right hand-mouth, and left hand-mouth
contacts resulting in a dose event, respectively. Di , Dj , and Dk , are the doses (in
units of virus) resulting from the ith fomes-mouth, jth right hand-mouth, and kth
left hand-mouth contacts, respectively. 3.5 is a beta-Poison function with shape (α)
and scale (N50 ) parameters equal to 0.265 and 5.597 plaque-forming units (PFU),
respectively (Haas et al., 1999).
The model accounts for both direct and indirect transmission routes. Direct transmission describes mouth-fomes contacts that transfer virus from the fomes to the
mouth (Equation 3.3). Indirect transmission describes hand transfer as intermediary between the fomes and the mouth, with hand-fomes contacts transferring virus
to the hand (Equation 3.2), and subsequent hand-mouth contacts resulting in dose
(Equation 3.3). Viral transfers are modeled as discrete contact events occurring at
intervals tF M , tRM , tLM , tRF , and tLF (s) describing subsequent fomes-mouth, right
hand-mouth, left hand-mouth, right hand-fomes, and left hand-fomes contacts, respectively. Viral inactivation continuously occurs on surfaces and hands, albeit at
different rates (kf and kh , respectively).
3.4
3.4.1
Methods and Materials
Parameter Estimation
The model parameters used to estimate a child’s dose due to interaction with a contaminated fomes include the initial concentration of virus on surface (Ci ), inactivation
rates of virus on surfaces (kf , kh ), percentage of virus transferred between contacted
surfaces (T Eom , T Eoh , T Ehm ), length of time between contact events (tF M , tRM , tLM ,
tRF , tLF ), surface area of fomes and hands (Af , Ah ), and surface area of contacts (Sf
, Sm , Sh ). For each parameter, we provide estimates, with justification, of values in
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
46
the form of approximated distributions and associated parameters. The distributions
and range of associated parameter values are summarized in Table 3.1.
Initial Concentration of Virus on Surface
Though multiple studies have demonstrated the presence of rotavirus genomic RNA
on indoor environmental surfaces using assays that require at least 100 rotavirus per
sample to detect, no work has quantitatively determined the concentration of rotavirus
on surfaces (Keswick et al., 1983; Wilde et al., 1992; Butz et al., 1993; Soule et al.,
1999) To reflect the uncertainty in the initial concentration of virus on a fomes and
the potential variability of the severity of contamination events, we used a uniform
distribution with minimum and maximum parameters of 0.001 and 10 virus/cm2 .
Inactivation Rates on Surfaces
Two inactivation rate parameters are required: rate of viral inactivation on dry environmental surfaces, kf , and rate of viral inactivation on hands, kh . Experimental
studies using MS2 phage as a surrogate for pathogenic virus were performed to estimate inactivation rates on environmental surfaces, kf . Glass slides (1 × 2.5 cm2 )
were inoculated with 107 PFU MS2 phage suspended in tryptic soy broth (TSB).
Borosilicate glass was chosen to represent a nonporous material and was prepared by
washing in soap and water, wiping with 70% ethanol, rinsing in distilled water, and
air-drying. TSB was used as the suspension media to include potential effects of particle shielding, though previous studies have demonstrated no significant difference in
the persistence of virus on fomites due to suspension media (Abad et al., 1994). After
inoculation, the surface samples were kept in 6-well plates at 20◦ C with 65% humidity
in the dark to provide a conservative estimate for viral inactivation on typical indoor
environments. The surfaces were swabbed with cotton-tipped applicators wetted in
500µl of phosphate buffered saline (PBS) at increasing intervals for a period of 50
days. The wetted cotton-tipped applicators were vortexed in microcentrifuge tubes
for 15 seconds in 500µl PBS, and 100µl of this was assayed using the double agar layer
method (USEPA, 2001). After swabbing, the surfaces were rinsed in 5 mL of PBS
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
47
for 15 minutes, and 100µl of the rinse was assayed to determine the concentration of
recoverable infective phage that were not removed by the cotton-tipped applicator.
The log of the total recovered (C) PFU over the log of initial count (C0 ) was plotted
as a function of time (t). Linear regression was used to determine the inactivation
rate (kf ) such that the concentration of viable, infective phage removable from the
surface of a fomes follows exponential decay (Equation 3.1).
We assume that the viral inactivation rate on hands, kh , follows an exponential
decay similar to previously reported inactivation rates for viruses on surfaces (Boone
and Gerba, 2007). However, kh has been shown to be greater than inactivation rates
on surfaces, possibly due to temperature and moisture or chemicals on the skin (Ansari
et al., 1988). Ansari et al. (1988) demonstrated a 93% reduction in rotavirus titer on
the surface of the skin over more than four hours, and this was used to estimate kh .
Percent Viral Transfer Between Surfaces
Three parameters describing viral transfer between surfaces are used: transfer between fomes and mouth (T EF M ), fomes and hand (T EF H ), and hand and mouth
(T EHM ). To estimate percent transfer during fomes and hand contacts, MS2 phage
was used as a surrogate virus in laboratory studies. Borosilicate glass and fingertips
were used as proxy surfaces. Four fingertips from each of 10 volunteers were inoculated with low (∼2×103 PFU) or high (∼2×104 PFU) titers of MS2 phage suspended
in TSB using a micropipettor. After the inoculation was allowed to dry, the fingertips
were placed against a glass surface for 10 seconds with an average constant pressure
of 25 kPa (range 16-38 kPa). The process was repeated with four glass surfaces inoculated to represent surface-to-hand transfer. For both directions of transfer, a fifth
surface (either finger or glass) was inoculated with PBS, representing a blank control.
Cotton-tipped swab applicators wetted in 500 µL of PBS were used to remove phage
from both the glass surface and the fingertip. The samples were stored at 4◦ C and,
within 48 hours, were vortexed for 20 seconds and enumerated using the double agar
layer technique (USEPA, 2001). This resulted in a total of 80 samples, not including
blanks, in four categories: low titer hand-to-surface, high titer hand-to-surface, low
titer surface-to-hand, and high titer surface-to-hand. The transfer of phage between
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
48
surfaces was quantified as the count of recoverable infective phage from the recipient
surface as a percentage of the total recoverable infective phage from both the recipient
and the donor surfaces (Rusin et al., 2002).
Micro-Level Activity Data
Five parameters are used in the model to describe a child’s discrete contact events
during his/her interactions with a toy: the time intervals between subsequent fomesmouth, right hand-mouth, left hand-mouth, right hand-fomes, and left hand-fomes
contacts (tF M , tRM , tLM , tRF , and tLF , respectively). A recent study determined that
the Weibull distributions best describe the frequency of contact event data (Xue et al.,
2007). As such, the time intervals in this study are described using the Weibull distributions (Law and Kelton, 1997). To determine the durations between subsequent
hand-toy (tRF and tLF ) and mouth-hand contacts (tLM and tRM ), we use data of
children’s interactions with their environment, as previously collected and described
(AuYeung et al., 2006; Ferguson et al., 2006). The data were collected by videotaping
one- to six-year-old children in both indoor and outdoor environments for 2-hour time
c software (SamaSama
periods. The videotapes were translated, using VideoTraq Consulting, Sunnyvale, CA, USA), into second-by-second accounts of the contact
events between a child’s right hand, left hand, and mouth and into 36 object categories for 20 children. The data set was subjected to quality control, as previously
described (Ferguson et al., 2006). The resulting micro-level activity data provide detailed descriptions of a child’s object contacts necessary for modeling the complexities
of intermittent contaminant loading and removal (Beamer, 2007). We assumed that
the time intervals between a child’s hand contacts with the object “Hard Toy” (defined as any hard, nonporous toy) could serve as a valid proxy for repetitive contacts
with a toy ball. In total, 1,340 right-hand and 1,433 left-hand contact events of 15 of
the 20 children were used to develop the Weibull distributions for tRF and tLF . The
time intervals for the remaining five children were used to cross-validate the Weibull
distributions using the Kolmogorov-Smirnoff test for goodness of fit.
The same method was used to determine the time intervals between hand-mouth
(tRM and tLM ) and fomes-mouth (tF M ) contacts. The number of data points used
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
49
to create the Weibull distribution were 129, 127, and 43 for right hand-mouth, left
hand-mouth, and fomes-mouth contacts, respectively.
3.4.2
Model Approach
The model is a discrete-time model, iterated by contact event. First, a sequence
of events describing fomes-mouth, right hand-mouth, left hand-mouth, right handfomes, and left hand-fomes contacts is simulated using a Monte Carlo sampling from
the interval distributions described by the respective parameters. An example of a
simulated sequence is shown as a time series in Figure 3.2, with solid vertical lines
representing contacts between the specified hand and the fomes, unfilled circles representing contacts between the hand and mouth, and filled circles representing contacts
between the fomes and mouth. Once a sequence of contacts is generated, the initial
and final concentrations of virus on each surface (left hand, right hand, and fomes)
are determined for each contact event using sampled virus-specific exposure factors,
dose-response parameters, and the specific equations in Appendix A, formulated from
Equations 3.1, 3.2, and 3.3. From this information, temporal exposure, dose, and risk
profiles are generated and metrics of interest are recorded. Examples of exposure and
dose profiles as a function of time are presented in Figure 3.3. The illustration of
the left hand in Figure 3.3 was omitted for simplicity. Vertical dashed lines represent
the timing of hand-fomes contacts, and vertical solid lines represent the timing of
hand-mouth contacts.
3.4.3
Sensitivity Analysis
The sensitivity analysis method used in this study was previously described by Xue
et al. (2006) Briefly, the model is run using single-point parameter values to investigate
the sensitivity of the model to variations in a given parameter. The model is run
twice, with the value of one specified parameter set first to the 25th percentile (p25)
and then to the 75th percentile (p75) of its probability distribution while all other
parameters remain set to their median values, and the model output is calculated. The
median, p25, and p75 values are used as normative values to describe distributions
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
50
because the model relies on multiple, different probability distribution functions. The
effects of parameter variation on output are investigated by calculating a ratio of the
cumulative dose resulting from the use of the p75 to the cumulative dose resulting
from the p25. The ratio of the results (p75:p25) quantifies the sensitivity of the model
to the parameter over the middle 50% of the probability distribution.
If p75:p25 is equal to 1, then the model output is unaffected by variations of
the parameter. A ratio greater than 1 demonstrates that increases in parameter
value, either from the p25 to the median or from the median to the p75, increase
the cumulative dose by a factor equal to the ratio. A ratio less than 1 demonstrates
a decrease in the cumulative dose by a factor equal to the inverse of the ratio. To
track the changes in model sensitivity to a given parameter as the length of time of
child-fomes interaction increases, we investigate the temporal change in the p75:p25
ratio for each parameter. The parameters are ranked by the order of influence on the
cumulative dose by comparing the absolute values of the log of the p75:p25 ratio.
3.5
3.5.1
Results
Parameter Estimation
Inactivation
The inactivation rate, kf , with 95% confidence interval was determined to be 0.0052
± 0.0014/h for the representative nonporous surfaces at ambient conditions (20◦ C
and 55-65% relative humidity). This value is within an order of magnitude of previously reported viral inactivation rates for rotavirus p13, astrovirus (serotype 4), and
hepatitis A (Boone and Gerba, 2007).
From Ansari et al. (1988), the inactivation rate of rotavirus on hands, kh , with
95% bootstrapped confidence interval was estimated to be 0.27 ± 0.03/h. This value
is within an order of magnitude of other studies investigating microbial inactivation
on the skin for other organisms (Musa et al., 1990; Traore et al., 2002) and is used in
this study as an estimate for viral inactivation on the skin.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
51
Transfer Efficiency
The transfer from glass to hand was determined to be 36%, with a standard deviation
(SD) of 26%, and the transfer from hand to glass was determined to be 27%, with
a SD of 23%. The Kruskal-Wallis test showed no statistical difference (p > 0.05) in
these populations, demonstrating insufficient evidence that the direction of transfer
influences percent transferred. Thus, our model assumes that the percentage of viral
transfer is direction-independent (Nicas and Sun, 2006). Additionally, there is insufficient evidence that virus transferred between surfaces is dependent on the initial viral
titer, as demonstrated by a Kruskal-Wallis test for significance (p > 0.05). Pooling
the data, T EF H used in this model is represented by a normal distribution, with a
mean of 32% and a SD of 25%. Although the mean is similar to reported values for
viral transfer (Ansari et al., 1988; Mbithi et al., 1992; Rusin et al., 2002), the spread
of this distribution is greater than previously reported for viral transfer, but is similar
to the values for lipophilic and nonlipophilic compounds (Cohen Hubal et al., 2008).
The transfer of virus between hand and mouth (T EHM ) was estimated using a
study by Rusin et al. (2002) examining PRD-1 phage transfer from fingertips to
lips (Rusin et al., 2002). Laboratory experiments investigating the transfer from 20
volunteers resulted in a mean transfer of 41% of recoverable phage onto the lips,
with no SD reported. We assume that the distribution spread (SD) for each percent
transfer parameter is similar to our experimentally determined distribution for T EF H ,
and therefore we assume a SD of 25% for T EHM as well. To our knowledge, no work
has yet investigated the amount of virus that transfers directly between a fomes and
mouth (T EF M ). We assume that this transfer (T EF M ) has a similar distribution as
the transfer of virus between hand and mouth: a normal distribution with a mean of
41% and a SD of 25%.
Micro-Level Activity Data
Using data from the videotapes, the time between subsequent right and left handfomes contact events, tRF and tLF , is modeled as aWeibull distribution with scale and
shape parameters 33 seconds and 0.62 for the right hand and 32 seconds and 0.63 for
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
52
the left hand, respectively. The Kolmogorov-Smirnoff goodness-of-fit test indicates
that the Weibull distribution is sufficient for describing 17 of 20 childrens right handfomes contacts and 16 of 20 childrens left hand-fomes contacts (p > 0.001). The
times between subsequent right and left hand-mouth contact events, tRM and tLM ,
are modeled as a Weibull distribution with scale and shape parameters of 270 s and
0.47 for the right hand (p > 0.05 for 20 of 20 children) and 420 s and 0.61 for the
left hand (p > 0.05 for 20 of 20 children), respectively. The time between subsequent
fomes-mouth contact events, tF M , is modeled as a Weibull distribution with scale and
shape parameters 140 s and 0.41 (p > 0.05 for 20 of 20 children), respectively.
Surface Area
Parameters describing the total surface area of the hand (AH ) and the fomes (AF )
are required. Additionally, surface areas of contact between the fomes and mouth
(SF ), the fomes and the hand (SH ), and the hand and mouth (SM ) are needed. We
estimated total surface area of a childs hand (AH ) as uniformly distributed with a
range of 270–390 cm2 , based on calculations using data available from the ChildSpecific Exposure Factors Handbook (interim report) (Tulve et al., 2002; USEPA,
2006). The toy ball is given a diameter of 9–11 cm, resulting in a uniform distribution
for surface area of the fomes (AF ), with a range of 250–380 cm2 . The literature values
were used for both surface area of contact between a fomes and a hand (SH ) and
surface area of contact between a hand and mouth (SM ) for children playing with
toys outdoors. The surface area between a fomes and a hand on contact were observed
to fall within the range of 8–27% of total hand surface area (AuYeung, 2007). For this
case study, a uniform distribution using the 5th and 95th percentiles (13–24%) of that
range as endpoints is used, and we assume that surface area of childs contact with
outdoor toys is a sufficient proxy for surface area of childs contact with indoor toys
(AuYeung, 2007). Similarly, the 5th and 95th percentiles of SM were estimated as a
range of 6–33% of total hand surface area (AuYeung, 2007). We assume a uniform
distribution using these values as endpoints,and that hand-mouth contacts outdoors
act as a sufficient proxy for similar childs contacts indoors.
To our knowledge, no data are available on the percentage of surface area of a
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
53
round ball contacted during fomes-mouth contacts. Therefore, we make a conservative
estimate that the surface area is similar to the surface area of childs contacts between
hand and mouth (6–33%). This estimate is conservative because, though we assumed
similar surface areas for the round ball fomes and the hand, less surface area will
likely be contacted during a mouthing event for a large round ball than for small,
irregularly shaped fingers and hands.
3.5.2
Model Results
Temporal Exposure, Dose, and Risk Estimates
Prototypical profiles of right- and left-hand exposures and virus concentration on
the fomes are provided in Figure 3.4. Virus concentration decreases on the fomes
as inactivation and continued hand-fomes and mouth-fomes contacts remove virus.
Conversely, virus concentration initially increases on the hands, as the presumed
original state of the hands is virus free. As described by Equation 3.2, the difference
in the concentration of virus between the fomes and hands drives the transfer of virus
between the two surfaces, forcing an eventual pseudoequilibrium between the surfaces.
Once this is reached, hand- and fomes-mouth contacts, combined with inactivation,
remove the virus from the fomes and hands, causing the concentrations to decrease
gradually.
Figure 3.5 displays the estimated cumulative dose and corresponding risk as a
function of time. The median cumulative dose increases approximately linearly with
the length of time the child interacts with the contaminated fomes, ranging from a
median dose of 13 virus (corresponding risk of infection (RI) of 60%) during 10 minutes
of interaction to 42 virus (RI 70%) during one hour of interaction. As rotavirus
has a low median infectious dose, or dose at which half of individuals exposed will
experience adverse health effects, of 5.6 PFU (Haas et al., 1999), the majority of risk
occurs within the first 10 minutes. This would not be true for other pathogenic agents
transmitted via the fecal-oral route such as Shigella and enteropathogenic Escherichia
coli, which have higher median infectious doses of 103 and 107 , respectively (Haas
et al., 1999), The 5th and 95th percentiles of dose, using the specified probability
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
54
distributions for each parameter, are 0 (RI = 0%) and 430 (RI = 84%) rotavirus with
10 minutes of child-toy interaction to 2 (RI = 35%) and 1,000 (RI = 87%) rotavirus
with one hour.
The virus concentration on the surface of the fomes is reflective of the degree of
severity of the contamination event, so we explored the dose and risk of illness as
a function of initial concentration on the fomes (Figure 3.6). As demonstrated in
the results of 1,000 model simulations of 10 minutes of child-fomes interaction with
best-fit functions (Figures 3.6a and 3.6B), there is large variability in the resulting
dose and risk of illness for a given initial virus concentration.
Despite this variability, the median dose linearly increases with the initial concentration on the fomes (Figures 3.6a and 3.6c). The dose and corresponding risk as a
function of initial concentration on the fomes for simulations between 10 and 60 minutes of child’s interaction with the fomes fits the beta-Poisson function (Figures 3.6b
and 3.6d), with a shape parameter (α) similar to that used for the beta-Poisson doseresponse model (Equation 3.5). The beta-Poisson scale parameter (equivalent to N50 )
describes the concentration on the fomes for which there is a 50% risk of illness (Haas
et al., 1999). The scale parameter decreases with increasing child-fomes interaction
time (from 0.3 PFU/cm2 at 10 minutes to 0.04 PFU/cm2 at 60 minutes), suggesting
the risk of illness from a given virus contamination increases the longer a child plays
with the toy, consistent with the previous observation given in Figure 3.5.
The relative importance of direct (fomes → mouth) and indirect (fomes → hand
→ mouth) transmission of viral pathogens was explored (Figure 3.7). As a child
initially interacts with the fomes, the fomes-mouth contacts contribute more than
80% of dose, demonstrating that a child’s direct mouthing of a toy is the most likely
route of viral transmission. As a child continues playing with a toy, the virus on
the fomes is transferred to the hands, and indirect transmission via mouthing hands
contributes more to the child’s total dose. Therefore, the proportion of total dose
from mouthing a toy decreases, and the proportion from mouthing hands increases.
Because the majority of the ingested dose occurs within the first 10 minutes, fomesmouth contacts contribute to the majority of a child’s risk of adverse health effects.
This may not be true for other pathogenic agents with higher median infectious doses
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
55
than rotavirus.
3.5.3
Sensitivity Analysis
Model-estimated dose relies on the stated assumptions concerning input parameter
values and distributions. Thus, we emphasize the relative importance of input parameters over absolute model output values (Zartarian et al., 2000) by examining the
parameter’s influence on model output through a sensitivity analysis.
The sensitivity analysis (Table 3.2) demonstrated that the parameters that most
influence a cumulative dose after 10 minutes of interaction are, in order: (1) initial
virus concentration on the surface of the fomes (Ci ), (2) frequency of fomes-mouth
contacts (tF M ), (3) frequency of right hand-mouth contacts (tRM ), (4) transfer of
virus between fomes and mouth (T EF M ), (5) frequency of left hand-mouth contacts
(tLM ), (6) percentage of the fomes that contacts the mouth on fomes-mouth contacts
(SF ), (7) surface area of fomes (AF ), and (8) percent transfer of virus between hand
and mouth (T EHM ). The commonly used Spearman correlation sensitivity analysis
(Gibbons, 1985; Siegel, 1988) supports these findings (data are not shown).
The sensitivity analysis method allows investigation of the changing influence of
each parameter over time (Table 3.2). As the child continues to interact with the
toy, the ratio of the dose resulting from the p75 to the p25 (p75:p25) value of a
parameter changes to reflect the parameters changing influence on resulting dose.
For example, the importance of fomes-mouth viral transfer decreases as child-fomes
interaction increases (Figure 3.8A). This further supports the finding that direct
contact between the fomes and mouth is the primary exposure route within the first
10 minutes, but that the indirect contacts between the hand and fomes and the hand
and mouth become increasingly important as the child continues interacting with the
toy. Similarly, the influence of the frequency of fomes-mouth contacts on resulting
dose decreases temporally (Figure 3.8B). The model also demonstrates that handfomes viral transfer, examined over the duration of the child-fomes interaction from
10 to 60 minutes, has little influence over resulting dose. When the percentage of
virus transferred on hand-fomes contacts is varied between 18% and 54%, the range
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
56
between the p25 and the p75, the change in dose is low. Presumably, this is because
the expected value of handfomes contact frequency (0.05/s) is an order of magnitude
larger than the median frequency of hand-mouth contacts (0.006/s). That is, there
are almost 10 hand-fomes contacts made for each hand-mouth contact. Because
multiple hand-fomes contacts occur, the virus concentration on the hands and fomes
reach equilibrium, regardless of the percentage of virus transferred on each individual
contact.
3.6
Implications
We use micro-level activity patterns in a mechanistic-stochastic model of dose to
more fully understand the role of fomites in pathogen transmission. We simulated
a single individuals interactions with a contaminated fomes, demonstrating the ability to model pathogen transmission on a contact-by-contact basis. Previous work
incorporating human-environment interactions has demonstrated the importance of
sequential contacts in understanding microbial exposure and risk from specific activities (Gibson et al., 1999; Chen et al., 2001; Gibson et al., 2002; Haas et al., 2005;
Nicas and Sun, 2006; Atkinson and Wein, 2008). We further this work by demonstrating the importance of modeling a wider range of activity level by incorporating
stochastic simulations of activity into microbial exposure assessment. Together with
previous studies investigating bacterial and viral transmission via contacts, this study
provides a foundation for incorporating human-environment interaction in dynamic
infectious disease models used to describe population-based pathogen transmission
for fecal-oral diseases (Elveback et al., 1971; Nasell, 2002; Stone et al., 2007).
An overarching goal of our work is to identify model parameters that require further study to improve future risk assessments. The model parameter most strongly
linked to estimated dose is, unsurprisingly, the concentration of virus on the fomes.Our
use of a uniform distribution for the initial concentration bounded by parameters
differing by over four orders of magnitude was motivated by not only a lack of quantitative data describing distributions of viral contamination on indoor surfaces but
also a desire to examine model output over the full range of plausible values. As
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
57
Laborde et al. (1994) found, the concentrations of indicator bacteria on surfaces in
child care centers regularly differed by over four orders of magnitude, emphasizing the
need to understand the exposure and risk resulting from interactions with surfaces
over a range of contamination levels (Laborde et al., 1994). Clearly, as the initial
concentration increases, one would expect the dose and risk of illness to increase as
well (Figure 3.6). Nevertheless, there is a need for further evaluation of the quantity
and spatial distributions of viral contamination on indoor surfaces, particularly for
surfaces such as toys that are most likely to act as fomites. This finding also reflects
the importance of reducing the presence of virus on surfaces to reduce or eliminate
fomes-mediated disease transmission.
The average time between the fomes and mouth contacts was a significant contributor to model output and should be investigated further. The importance of this
parameter decreased temporally as the child continued interacting with the toy. This
is explained by the increased contribution of right hand-mouth and left hand-mouth
contacts to dose (Figure 3.7). Implementing an intervention to reduce fomes-mouth
contacts would result in a reduction in an individuals dose. A decrease of 50% of the
rate of fomes-mouth contacts, with all other parameters unchanged in the stochastic
model, reduced the median dose by 31%, with a corresponding reduction of risk for
rotavirus of 4%, after 10 minutes of child-fomes interaction. However, modifications
in a childs behavior may be difficult or impossible to implement.
The amount of viral transfer between surfaces may be an example of a parameter
that could be readily modified, as different toy surface properties or environmental conditions may influence the amount of virus transferred between surfaces. As
demonstrated, the transfer of virus between fomes and mouth is the fourth most influential parameter in determining dose and risk. Reduction in the transfer between
fomes and mouth by 50%, without any changes in the values or distributions of the
other variables in the stochastic model, reduces the median dose of the simulations
by 22%, with a corresponding reduction of risk of 2%, after 10 minutes of interaction.
This highlights the importance of better understanding the effect of environmental
conditions on viral transfer between surfaces to reduce the incidence of disease, as
environmental conditions are easily modified via the use of humidifiers/dehumidifiers
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
58
and thermostats. Additionally, the use of toys with surface properties that inhibit
or reduce viral transfer during periods when viral outbreaks are most likely to occur
may reduce or slow the transmission of disease (Nicas and Sun, 2006).
Finally, the model demonstrates that viral inactivation plays little or no role in
determining an individuals dose over the time scale of our model. Reduction of viral
inactivation on both fomes and hands by 100% (i.e., assuming no viral inactivation)
resulted in a change in the median dose of one viral particle, with corresponding
difference in risk of only 0.5% after 10 minutes of interaction. After 60 minutes of
interaction, assuming no viral inactivation, the median dose changed by only four
viral particles, with corresponding difference in risk of 0.5%. This result applies
only to ambient environmental conditions and the time scale of this model, which
focuses on child’s interaction with the fomes at a temperature of 20◦ C and relative
humidity of 65% over a period of at most 60 minutes. Changes in environmental
factors significantly alter inactivation rates of virus on surfaces, with rapid increases in
inactivation rates at a higher relative humidity and temperature (Ansari et al., 1991).
Viral persistence on surfaces likely influences transmissibility via fomites on a time
scale more closely aligned with the time scale of inactivation rates, for example, days
or weeks (Boone and Gerba, 2007), and at more extreme environmental conditions,
for example, relative humidity >85% and temperature >30◦ C (Ansari et al., 1991).
The results of the model rely on assumptions and simplifications. For example,
we assumed uniform distribution of viral agents on both fomes and hands, before
and after contacts. This assumption, common in chemical exposure modeling and in
investigation of aerosol dispersion of pathogenic agents (Nazaroff et al., 1998; Zartarian et al., 2000; Liao et al., 2008), clearly impacts the resulting simulated risk of
adverse health effects. Future work should examine uneven pathogen distribution
on surfaces. Additionally, the model does not explore the effects of environmental
conditions, such as temperature and humidity, on the transmission and inactivation
of virus. As temperature and humidity have recently been implicated in the seasonal
patterns of influenza transmission (Lowen et al., 2007), future work could focus on
discerning the effect of temperature and humidity on inactivation and transfer to
elucidate their role in the transmission of fecal-orally transmitted viruses.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
59
Examination of an individual’s interactions with his/her environment to assess exposure to infectious disease lays the groundwork for incorporating activity and virusspecific exposure factors into broader, secondary transmission models. Although the
model incorporates individual’s contact events and virus-specific exposure factors such
as transfer of virus between surfaces and viral inactivation rates in disease transmission, the model is limited to assessing a static examination of a single individual’s
risk. The dose-response model used to determine an individual’s risk was based on
data obtained from healthy adults; we did not account for the uncertainty associated in applying this model to children to determine the risk of illness (Ward et al.,
1986). Similarly, the model does not account for immunity or interaction between
multiple children, which would enable the dynamic modeling of secondary transmission via person to person or person to fomes to person (Abbey, 1952; Elveback et al.,
1971; Nasell, 2002; Lawniczak et al., 2006; Giraldo and Palacio, 2008). Despite the
limitations of the model, we elucidate the roles of an individual’s contact events, viral inactivation, and viral transfer on an individual’s risk of adverse health effects.
This mechanistic-stochastic model of microbial dose incorporates contact-by-contact
human-environment interactions and can therefore serve as a basis for future highresolution microbial risk assessment.
3.7
Acknowledgments
The authors acknowledge the volunteers, members of the Boehm and Leckie research
groups, and anonymous reviewers who assisted with the work and/or provided suggestions for improving the article. This publication was supported by the Stanford
University Shah Research Fellowship and the STAR Research Assistance Agreement
No. F07D30757 awarded by the U.S. Environmental Protection Agency (EPA). It
has not been formally reviewed by the EPA. The views expressed in this article are
solely those of the authors, and the EPA does not endorse any products or commercial
services mentioned in this article.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
3.8
Tables
60
kf
kh
AF
AH
tF M
tRF
tLF
tRM
tLM
SF
SH
SM
T EF M
T EF H
T EHM
Inactivation Rate
Fomes
Hand
Area of Object
Fomes
Hand
Contact Frequency
Fomes and Mouth
Right Hand and Fomes
Left Hand and Fomes
Right Hand and Mouth
Left Hand and Mouth
Percent of Object Contacted
Fomes (Fomes-Mouth Contact)
Hand (Fomes-Hand Contact)
Hand (Hand-Mouth Contact)
Percent Transferred
Between Fomes and Mouth
Between Fomes and Hand
Between Hand and Mouth
%
%
%
%
%
%
s
s
s
s
s
cm2
cm2
1/s
1/s
virus/cm2
Units
(140, 0.41)
(33, 0.62)
(32, 0.63)
(420, 0.59)
(270, 0.47)
Normal (0.41, 0.25)
Normal (0.36, 0.26)
Normal (0.41, 0.25)
Uniform (0.06,0.33)
Uniform (0.13,0.24)
Uniform (0.06,0.33)
Weibull
Weibull
Weibull
Weibull
Weibull
Uniform (250,380)
Uniform (270,390)
Normal (1.4×10−6 , 2.0×10−7 )
Normal (7.5×10−5 , 4.3×10−6 )
Uniform(0.001,10)
Distribution (Parameters)
Study
Study
Study
Study
Study
Assumption
This Study
Rusin et al. (2002), Assumption
Assumption
AuYeung (2007)
AuYeung (2007)
This
This
This
This
This
Assumption
USEPA (2006)
This Study
Ansari et al. (1988)
Assumption
Source/Reference
Table 3.1: Parameters Used in Model, with Corresponding Distributions and Median Values for Determining
Exposure and Dose Distributions
Ci
Symbol
Virus Concentration
Initial Concentration
Variable Description
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
61
0.001
1.27×10−6
7.21×10−5
283
300
6.50×10−4
5.10×10−3
5.50×10−3
4.80×10−4
4.60×10−4
0.13
0.16
0.13
0.24
0.18
0.24
Virus concentration
Initial concentration
Inactivation rate
Fomes
Hand
Area of object
Fomes
Hand
Contact frequency
Fomes and mouth
Right hand and fomes
Left hand and fomes
Right hand and mouth
Left hand and mouth
Percent of object contacted
Fomes (fomes-mouth contact)
Hand (fomes-hand contact)
Hand (hand-mouth contact)
Percent transferred
Between fomes and mouth
Between fomes and hand
Between hand and mouth
0.41
0.36
0.41
0.20
0.19
0.20
1.60×10−3
1.25×10−2
1.32×10−2
1.20×10−3
1.10×10−3
315
330
1.40×10−6
7.50×10−5
0.1
p50
0.58
0.54
0.58
0.26
0.21
0.26
3.20×10−3
2.50×10−2
2.60×10−2
2.40×10−3
2.20×10−3
348
360
1.53×10−6
7.79×10−5
10
p75
1.9
0.9
1.3
1.6
1.0
1.2
3.0
1.0
1.0
1.2
1.2
1.2
1.0
1.0
1.0
10000
10 min
1.6
0.9
1.3
1.5
1.0
1.3
2.3
1.0
1.0
1.4
1.3
1.3
1.0
1.0
1.0
10000
20 min
1.5
0.9
1.4
1.4
1.0
1.3
2.1
1.0
1.0
1.4
1.3
1.3
1.0
1.0
1.0
10000
30 min
1.5
1.0
1.4
1.4
1.0
1.3
2.0
1.0
1.0
1.4
1.4
1.3
1.0
1.0
1.0
10000
40 min
p75:p25
1.4
1.0
1.5
1.3
1.0
1.4
1.9
1.0
1.0
1.4
1.4
1.3
1.0
1.0
1.0
10000
50 min
1.4
1.0
1.4
1.3
1.0
1.4
1.8
1.0
1.0
1.4
1.4
1.3
1.0
1.0
1.0
10000
60 min
8
3
4
5
6
2
7
1
Rank
Table 3.2: Sensitivity Analysis Results for Cumulative Dose (Number of Virus) During Increasing Length of Time
of Child-Fomes Interaction
p25
Variable Description
Input Values
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
62
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
3.9
Figures
63
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
64
Figure 1
4: Inactivated
2: Right
Hand
5: Facial
Membrane/
Dose
1: Fomes
3: Left
Hand
Legend
- Viral Inactivation
- Transfer by Contact
Figure 3.1: The relationships between the five potential reservoirs for virus represented by this model. At time 0, the fomes is the only contaminated object, and the
right and left hands are free of virus. The arrows represent the possible pathways
between the states.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
65
Figure 2
Right Hand
Left Hand
Fomes
0
Legend
100
200
300
- Hand-Fomes Contact
400 500 600
Time (s)
- Hand-Mouth Contact
700
800
900 1000
- Fomes-Mouth Contact
Figure 3.2: Example of timing for randomly generated sequence of contact events
between left hand, right hand, fomes, and mouth. Vertical solid lines represent handfomes contacts, unfilled circles represent hand-mouth contacts, and filled circles represent fomes-mouth contacts.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
66
Figure 3
Right Hand Conc. (Ch) Fomes Conc. (Cf )
200
150
100
100
50
20
40
60
80
150
100
100
50
40
60
80
Dose (D)
20
Time
0
Legend
0 0
- Hand-Fomes Contact
- Hand-Mouth Contact
- Dose
Figure 3.3: Example of trends of concentration, exposure, and dose over time, simulated from randomly generated sequence of contact events, describing interaction
between right hand, fomes, and mouth. Vertical dashed lines represent right handfomes contacts, and vertical solid lines represent right hand-mouth contacts. Each
hand-fomes contact results in virus transfer between fomes and hand. Each handmouth contact results in virus transfer from hand to mouth. Different inactivation
rates (kh and kf ) continuously decrease viral concentration on hand and fomes.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
67
Figure 4
10
Fomes
Right Hand
Left Hand
2
Virus Concentration (virus/cm )
10
8
8
6
6
4
4
2
2
0
0
0
10
10
20
20
30
30
40
40
50
50
0
6060
Length of Time of Child-Fomes Interaction (min)
Figure 3.4: Example of modeled concentration and exposure profiles for fomes, right
hand, and left hand, demonstrating the temporal change in concentrations.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
68
Cumulative Ingested Dose (virus)
Figure 5
1200
1000
800
600
400
200
0
10
20
30
40
50
60
10
20
30
40
50
60
Risk of Illness (%)
100
80
60
40
20
0
Time of Child-Fomes Interaction (min)
Figure 3.5: Modeled distributions of (top panel) dose and (bottom panel) risk of
infection from 10,000 simulations of child-fomes interaction after specified interaction time. Boxes depict the median, 25th percentile, and 75th percentile. Whiskers
represent the 5th and 95th percentiles.
Figure 6
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
1200
(a)
1000
1.0
0.9
800
(b)
0.8
0.7
Simulation Results
Best Fit Line
69
Simulation Results
Best Fit Line
0.6
0.5
600
0.4
400
0.3
0.2
0
-3.0 -2.5 -2.0 -1.5 -1.0 -0.5
0.0
0.5
1.0
(c)
1200
1000
800
600
60 min
50 min
40 min
30 min
20 min
10 min
0.1
0.0
-3.0 -2.5 -2.0 -1.5 -1.0 -0.5
1.0
0.9
0.8
0.7
0.6
0.5
0.4
400
0.0
0.5
0.0
0.5
1.0
(d)
60 min
50 min
40 min
30 min
20 min
10 min
0.3
0.2
200
0
-3.0
Risk of Illness
Ingested Dose (virus)
200
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
0.1
0.0
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
1.0
Initial Virus Concentration on Fomes (Log(virus/cm2 ))
Figure 3.6: Dose and risk of illness as a function of initial concentration of virus on
fomes. Best-fit lines for (a) and (c) are calculated using the linear relationship of
resulting dose from the initial concentration on fomes from 10,000 model simulations.
Best-fit lines for (b) and (d) are calculated using the beta-Poisson distribution resulting from the risk of illness as a function of initial concentration on fomes from 10,000
model simulations. (a) Dose as a function of initial concentration resulting from 1,000
model simulations plotted with the best-fit line for 10 minutes of child-fomes interaction demonstrates the variability of modeling results and distribution around the
best-fit line. (b) Risk of illness as a function of initial concentration resulting from
1,000 model simulations plotted with the best-fit line for 10 minutes of child-fomes
interaction. (c) Dose increases as a function of child-fomes interaction as well as initial concentration on fomes, as shown in 10-minute increments for 10–60 minutes of
child-fomes interaction. (d) Risk of illness exhibits increases similar to those of dose
as a function of initial concentration on fomes, as shown in 10-minute increments for
10–60 minutes of child-fomes interaction.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
70
Figure 7
Ingested Dose (virus)
21%
50
20%
30
20
15%
8%
11%
67%
60% 21%
18%
81%
s-M
Fome
10
0
56% 23%
58% 23%
19%
40
22%
54% 24%
outh
acts
Cont
Right Hand - M
outh Contacts
Left Hand - Mouth Contacts
10
20
30
40
50
60
Time (min)
Figure 3.7: Dose contributions over time from left hand-, right hand-, and fomesmouth contacts increase as child-fomes interaction increases, as demonstrated by the
line graph. The pie charts demonstrate the percentage of dose contribution from
left hand-, right hand-, and fomes-mouth contacts at the specified time, with the
contribution from fomes-mouth contacts decreasing as a percentage of the whole as
child-fomes interaction increases.
CHAPTER 3. ROTAVIRUS EXPOSURE MODEL
71
Ratio of Cumulative Dose (p75:p25)
Figure 8
(a) 2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
(b) 5
4
3
TE Fomes-Mouth
TE Hand-Mouth
TE Hand-Fomes
5
t Fomes-Mouth
4 t Hand-Mouth
t Hand-Fomes
3
2
2
1
1
0
0
10 20 30 40 50 60
Length of Time of Child-Fomes Interaction (min)
Figure 3.8: Sensitivity analysis examining the effect of parameter variation on model
output as it changes over time. The ratio of the cumulative dose calculated using
the 75th percentile value of the probability distribution to the cumulative dose calculated using the 25th percentile value of the specified parameter. The ratio of the
cumulative dose refers to the factor by which it changes when the parameter value
is increased for: (a) viral transfer between surfaces, where T EF omes−M outh is the percentage of virus transferred from fomes to mouth per contact, T EHand−M outh is the
percentage of virus transferred from hand to mouth per contact, and T EHand−F omes
is the percentage of virus transferred between hand and fomes per contact, and (b)
the frequency of contact events, where tF omes−M outh is the frequency of fomes-mouth
contacts, tHand−M outh is the frequency of right hand-mouth contacts, and tF omes−Hand
is the frequency of fomes-right-hand contacts.
Chapter 4
Surface Sampling Methods for
Virus Recovery From Fomites
The results presented in this chapter will be submitted to a peer reviewed journal in
Winter 2011. Francisco J. Tamayo contributed to the experimental design and data
collection, and will be co–author on the resulting publication. James O. Leckie and
Alexandria B. Boehm will also appear as co–authors, for their contributions to study
design, data interpretation, and manuscript improvements.
72
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
4.1
73
Abstract
The role of fomites in infectious disease transmission relative to other exposure routes
is difficult to discern due, in part, to the lack of information on the level and distribution of virus contamination on surfaces. Comparison of outcomes of studies intending
to fill this gap is difficult because multiple different sampling methods are employed
and authors rarely report their method’s lower limit of detection. In the present
study, we demonstrate that the sampling method significantly influences virus recovery from surfaces, and therefore influences study outcomes. We compare sampling
methods chosen from a literature review to identify the most efficient method for recovering virus from surfaces in a laboratory trial using MS2 bacteriophage as a model
virus. Recovery of virus is determined using both plaque assay and quantitative polymerase chain reaction. From this, we conclude that polyester-tipped swabs prewetted
in either 1/4–strength Ringer’s solution or saline solution most effectively remove
virus from nonporous fomites. Our results also demonstrate that the recommended
sampling method is an appropriate method for quantifying virus on surfaces.
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
4.2
74
Introduction
Preclusion of infection is the most effective method to combat the respiratory and
gastrointestinal diseases that cause over 6 million annual deaths, worldwide (Boone
and Gerba, 2007; Mathers et al., 2008). Successful interventions to reduce disease
burden include hand and environmental hygiene (Siegel et al., 2007; Bell, 2006), but
the impact of these interventions is difficult to quantify because the importance of
contact with contaminated surfaces, or fomites, relative to other transmission routes
is uncertain (Mubareka et al., 2009; Brankston et al., 2007).
Evidence of the importance of fomites comes from both laboratory and field studies. Laboratory studies have demonstrated that handling either artificially–or naturally–contaminated fomites by susceptible hosts indoors results in subsequent infection (Gwaltney, 1982; Hall et al., 1980). Additionally, virus can be transferred
between hands and fomites on contact, and survive on fomites for hours or days
(Bean et al., 1982; Rusin et al., 2002; Abad et al., 1994). In a field study, environmental hygiene as an intervention significantly reduced illness–related absenteeism in
classrooms (Bright et al., 2009). Additionally, fomites, such as carpets (Osterholm
et al., 1979; Evans et al., 2002), towels, and medication cart items (Morens and Rash,
1995) have been implicated as the primary cause of multiple outbreaks. Despite this
evidence, questions remain regarding relative efficacy of fomite–mediated transmission relative to other exposure routes (Jennings and Dick, 1987; Atkinson and Wein,
2008) and likelihood of virus transfer from fomites to hosts (Pappas et al., 2009).
Surface contamination is most often described by the positivity rate, defined as
the fraction of total samples collected on which the organism is detectable (Butz
et al., 1993). However, the positivity rate does not provide an indicator of infection
risk, which depends on exposure magnitude (Haas et al., 1999) and therefore requires
information about the quantity of virus on the surface. Virus quantity on a surface,
expressed as number of virus or virus equivalents per unit area, has only been measured in a few studies (Bellamy et al., 1998; Russell et al., 2006; Piazza et al., 1987).
Moreover, positivity rate is influenced by the sampling method and detection assay:
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
75
more sensitive sampling methods and detection assays will yield increases in positivity rates even though the actual level of virus contamination may be unchanged.
Use of a sensitive, standard method would limit bias introduced by various sampling
methods.
Two previous studies have compared virus surface sampling methods and suggested that implement type (the tool used to collect the sample, such as a swab) and
eluent type (the liquid used to aid in removal, such as saline solution) significantly
influence virus recovery. Carducci et al. (2002) recovered a greater fraction of hepatitis C virus from a seeded surface using beef extract than using bovine serum albumin
when swabbing with a cotton–tipped applicator. The study demonstrated that eluent type can significantly impact virus recovery from surfaces. Similarly, Taku et al.
(2002) demonstrated the impact of implement type by comparing calicivirus recovery
from food surfaces for four sampling methods. Rinsing a surface in 0.05 M glycine
buffer, rubbing with a cell scraper, then aspirating the buffer was recommended over:
1) rinsing surface in buffer then aspirating, 2) swabbing surface with cotton–tipped
applicator, or 3) swabbing surface with a nylon filter. However, Taku et al. (2002)
recommended method is not easily adapted to the geometry of most fomites. Further research is needed to refine implement and eluent choice for sampling fomites to
maximize virus recovery.
In the present study, we systematically review the literature on virus sampling
of fomites and use an extensive laboratory–based trial to compare methods of virus
detection on surfaces. We identify, summarize, and analyze 45 articles that include
unique data sets on virus detection on surfaces. The most commonly used and most effective sampling methods identified from the meta–analysis are compared in a laboratory–based study for removal of bacteriophage MS2, as measured using culture–based
and quantitative reverse–transcription polymerase chain reaction (qRT–PCR), from
plastic and stainless steel surfaces. Using both the literature review and experimental
results, we identify polyester–tipped swabs prewetted in either 1/4 strength Ringer’s
solution (heretofore referred to as “Ringer’s”) or saline solution as the most effective
buffer and implement combination to remove virus from nonporous fomites.
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
4.3
4.3.1
76
Materials and Methods
Review of Virus Surface Sampling Literature
Relevant articles were identified by searching the PubMed database on 5 February
2010 with keywords: “virus” and one of the following: 1) “fomite(s)”, 2) “environmental contamination”, 3) “environmental surface(s)”, or 4) “environmental sample(s)”
and “surface(s)”.
Only articles written in English were considered. Of those identified, the articles
included in the review fit the following criteria: 1) included original data collected
from environmental surfaces, where clinical (e.g. skin, bodily fluids) and food (e.g.
meat, vegetables) surfaces were not considered, and 2) tested samples for human
pathogenic virus or fecal indicator bacteriophage (e.g. somatic, F+ bacteriophage).
To identify articles not included in PubMed, the citations of the articles fitting the
criteria were also reviewed.
For analysis, data from the articles were separated into data sets according to the
virus and the presence/absence of a clinically infected individual. That is, articles
that reported positivity rates for multiple viruses were split into separate data sets for
each virus. Similarly, articles that sampled surfaces during periods that encompassed
both presence and absence of clinically infected individuals were split into separate
data sets for each time period. Samples collected two weeks before and after at least
one individual was identified as clinically infected were considered separately than
samples collected where no clinically infected individual was present. In this manner,
seventy–four data sets from forty–five articles were obtained.
Positivity rate was determined, as the outcome variable, for each data set. Positivity rate was the only feasible outcome variable as most (96%) of the studies identified
reported presence/absence of virus on surfaces. Only a few (3 of 74, or 4%) reported
quantitative data. If the authors included clinical or food samples, those samples
were removed. To allow for logit–transformation, the positivity rate for studies that
detected the virus on none or all of the samples was adjusted to detection limits of 1/n
or (n − 1)/n, respectively, where n is the study’s total number of samples collected.
The positivity rate is an inherently biased outcome variable because the lower limit
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
77
of detection (LLOD) likely varies across studies for reasons described previously. As
few studies (21%) reported either the quantitative concentration of the virus or the
LLOD of the sampling method, the positivity rate could not be adjusted to account
for the bias.
We assessed the influence of the implement and eluent used to collect and analyze
the samples on positivity rate. Similar implement and eluents were grouped for data
analysis. Polyester and Dacron swabs were both categorized as polyester. The eluent
used was categorized into one of four groups: media (defined here as any eluent with a
carbon source, and includes Amies medium, beef extract, brain heart infusion broth,
Letheen broth, minimum essential medium, RPMI–1640, and tryptose phosphate
broth with 0.5% gelatin), saline (defined as any isotonic eluent without a carbon
source, and includes phosphate buffered saline, 0.8% saline, and Ringer’s solution),
water, or unreported. Additives and constituents of eluents, such as antibiotics,
were ignored for data analysis with the lone exception of calcium. We examined
whether or not the presence of calcium in the eluent influenced positivity rate, where
calcium is present in Ringer’s solution, Amies medium, minimum essential medium,
and RPMI–1640.
Statistics
The positivity rate for each study was logit–transformed, and normality of transformed data was assessed using the Shapiro–Wilk test to support use of parameteric
statistics. Two bivariate linear models were used to determine the significance of implement and eluent choice, separately, on transformed positivity rate; positivity rate
was weighted by total number of samples in each study. To determine effect of trace
calcium in the eluent on positivity rate, a χ2 test for equal proportions was used.
All statistics were performed using R (version 2.9.0, R Foundation for Statistical
Computing, Vienna, Austria).
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
4.3.2
78
Laboratory–Based Surface Sampling Method Comparison
In a laboratory–based trial, we compared fraction of virus recovered from surfaces
using a subset of the implement and eluent choices identified in the literature as most
commonly used and most effective.
Virus and preparation of inoculum
MS2 bacteriophage was obtained from the American Type Culture Collection
(ATCC). MS2 (ATCC #15597–B1) is a +–sense RNA virus with a icosahedral,
tailless, capsid of 27 nm in diameter. The isoelectric point (pI) of MS2 is 3.9. MS2
bacteriophage was chosen because of its prior use a surrogate for human viruses,
such as norovirus, (Dawson et al., 2005) and the availability of plaque assay and
qRT–PCR methods to enumerate both infective phage and copies of nucleic acids
(USEPA, 2001; O’Connell et al., 2006). E. coli HS(pFamp)R (ATCC #700891) was
used to propagate and enumerate MS2.
The inoculum used in the study was prepared according to the polyethylene glycol precipitation method (Pecson et al., 2009). The propagated virus was then enumerated using the double agar layer method and diluted in dilution buffer (5 mM
NaH2 PO4 , 10mM NaCl, pH = 7.4) to 1 × 104 PFU/ml to be used as virus stock. Immediately before being seeded on the surface, the virus stock was mixed with tryptic
soy broth to form a 50% solution.
Implement and Eluents Tested
The two most commonly used, and the single most effective (highest mean positivity
rate) implements and eluents were identified from the literature for use in the laboratory study. The implements tested included the cotton–tipped and polyester–tipped
swabs as the most commonly used (used in 39 and 12, respectively, of the 74 studies)
and antistatic cloth as the most effective (positivity rate of 0.408). Similarly, the eluents tested included saline and viral transport media (used in 18 and 9, respectively,
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
79
of the 74 studies) as the most common and Ringer’s solution (used in the same article
as the antistatic cloth) as the most effective.
A fourth eluent, termed acid/base, was added to assess a novel combination
adapted from a method to concentrate virus from environmental water samples
(Katayama et al., 2002). The acid/base eluent relies on knowledge of the virus
surface charge to improve recovery from surfaces. This study is the first use of the
eluent combination to remove virus from surfaces. Briefly, a weakly acidic (0.5 mM
dihydrogen sulfate (H2 SO4 )) eluent is used to wet the implement prior to sampling.
Viruses with low isoelectric points adsorb to negatively–charged surfaces (like cotton)
under acidic conditions (Katayama et al., 2002). After sampling, the implement is
placed into a weakly basic (1 mM sodium hydroxide, pH 10.5–10.8) eluent which
reverses the surface charge of the virus to elute the virus from the implement.
Surfaces Tested
To determine the method most effective in removing virus from surfaces, we compared recovery from both high temperature polyvinyl chloride (PVC) plastic (Part No.
8748K21) and type 304 stainless steel with a mirror–like finish (Part No. 9785K11),
both obtained from McMaster–Carr (Santa Fe Springs, CA, USA). Many of the surfaces identified in the literature review that were frequently contaminated (e.g. door
knobs, faucet handles, drains, medical instruments, toys, playmats, computer parts,
telephones) were composed of plastic or metal. PVC plastic and stainless steel, in
930cm2 square sheets, were chosen as representative samples as it was infeasible to
test every potential surface. 10 replicates for each eluent and implement combination
were tested on both surfaces. In total, 240 samples were collected (3 implements, 4
eluents, 2 surfaces, and 10 replicates). All were tested using the double agar layer
plaque assay method, and a subset using qRT–PCR.
Study Design
For both plastic and stainless steel surfaces, a 5 µl inoculum of approximately
4900–5200 PFU bacteriophage was seeded in the center of 120 5 cm × 5 cm surface
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
80
swatches. The seeded aliquot was dried for 45 ± 1 minutes under ambient conditions (temperature 20–22◦ C and relative humidity of 45–60%, determined by a
thermometer and hygrometer (Springfield Precision Instruments, Wood Ridge, NJ)).
The order of implement and eluent combinations used to recover bacteriophage
from the surfaces was randomized prior to the start of the study. The polyester and
cotton–tipped swabs were obtained from Fisher Scientific (Thermo Fisher Scientific,
Waltham, MA, USA). The antistatic cloths were obtained from Bel–Art Products
(Pequannock, NJ, USA) and cut into single–ply square swatches of approximately 9
cm2 . The eluents used were Ringer’s solution (EMD Chemicals, Inc, Gibbstown, NJ,
USA), 0.85% saline solution, virus transport media (Copan Diagnostics, Murietta,
CA, USA), and acid/base.
Centrifuge tubes (15ml, BD Biosciences, San Jose, CA) were filled with 1.5 ml of
0.85% saline, viral transport media, Ringer’s solution, or 1 mM sodium hydroxide. To
sample, the polyester or cotton–tipped swabs were wetted in the eluent (or in 0.5 mM
dihydrogen sulfate, for acid/base) and then rubbed with moderate and consistent
pressure across the surface first horizontally, then vertically, then diagonally for a
total of 10 s. The swab was then placed into the centrifuge tube, and the tube was
capped and stored on ice for 4 hours to mimic typical transportation time. Antistatic
cloth, otherwise following the same procedure, was not wetted prior to sampling.
After storage, the samples were vortexed for 60 s. An aliquot of 100 µl was used
to assay the samples for infective bacteriophage using the double agar layer method
(USEPA, 2001). The remaining sample was stored at –80◦ C.
qRT–PCR
Viral recovery was determined using qRT–PCR from plastic and stainless steel surfaces for only two implement/eluent combinations: cotton–tipped swab in saline
solution and polyester–tipped swab in Ringer’s. Cotton/saline was the most common implement/eluent combination used in studies reviewed in the meta–analysis;
polyester/Ringer’s was the combination with highest efficacy of recovery measured using the culture–based assay (to be shown). Twenty–eight samples were assayed using
qRT–PCR: seven samples for each combination of cotton/saline or polyester/Ringer’s
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
81
and plastic or stainless steel surface. RNA was extracted and quantified from 200 µl
of sample volume, after storage at –80◦ C for 15–20 days following sample collection.
qRT–PCR was performed on the extracts within 6 hours.
To extract viral RNA, we used the Invitrogen PureLink Viral RNA/DNA extraction kit (Invitrogen, Carlsbad, CA) according to manufacturer’s instructions using
200 µl samples eluted in 20 µl of DNase/RNase–free water. Genomic RNA was
enumerated using the Taqman quantitative reverse–transcption polymerase chain reaction (qRT–PCR) with reagents, primers, and cycling conditions of O’Connell et al.
(2006) for a 25 µl reaction with 5 µl template (O’Connell et al., 2006). The location
in the MS2 genome of the primers, probe, and target (the sequence of the qRT–PCR
amplicon), is the RNA replicase β chain.
The forward primer (5’–GCTCTGA-
GAGCGGCTCTATTG–3’), reverse primer (5’–CGTTATAGCGGACCGCGT–3’),
and probe (5’–[FAM]–CCGAGACCAATGTGCGCCGTG–[TAMRA]–3’) were obtained from Eurofins MWG Operon (Huntsville, AL) (O’Connell et al., 2006). RNA
standards were created from total genomic RNA extracted without aid of transfer
RNA from a high titer of purified MS2 bacteriophage, enumerated as 20 ng/µl using
a NanoDrop ND–1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA,
USA) and diluted to four standards, at 10–fold dilution, between 0.1 fg/µl (50
genome equivalents / µl) and 10000 fg / µl (50000 genome equivalents / µl). A
genome of 3569 nucleotides with average molecular mass of 330 Da per nucleotide
was assumed to convert RNA concentration to genome equivalents (O’Connell et al.,
2006). qRT–PCR was performed using a StepOne Plus RealTime PCR System
(Applied Biosystems, Carlsbad, CA) and all samples and standards were run in
triplicate.
Statistics
Descriptive statistics (mean, median, standard deviation) are provided for recovery
of infective bacteriophage from each surface using each method. To determine the
implement and eluent choice that most effectively removes bacteriophage from surfaces, an n–way ANOVA with post–hoc Tukey’s test was performed on untransformed
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
82
data. Surface sampled, implement, and eluent are the independent variables; fraction of recovered infective bacteriophage is the dependent variable. Significance was
determined if the p–value ≤ 0.05. Fraction of target RNA recovered was defined as
target copies of RNA enumerated for each sample divided by the number of target
copies seeded. Linear regression was used to model the relationship of the number
of target copies estimated from qRT–PCR as a function of the number of infective
bacteriophage estimated using the double agar layer method, with an intercept set at
0. Variables for surface swabbed (plastic or stainless steel) and for implement/eluent
combination were included in the regression model.
4.4
4.4.1
Results
Literature Review
310 articles were identified in the keyword search using PubMed. Of those, 40 fit the
specified criteria. Review of the citations revealed an additional 5 relevant articles. In
total, 45 relevant articles were identified. Eleven of the articles sampled for multiple
pathogens and/or during time periods where a clinically infected individual was and
was not present. As a result, the articles are treated as 74 separate studies. A
summary of the articles, including the separation into studies, implement, eluent,
and assay used, positivity rate, and locale are provided in Table B.1. Definitions of
abbreviations used in Table B.1 are provided in Table B.2.
Authors measured environmental contamination of 20 different etiologic agents,
including causative agents of gastrointestinal, respiratory, bloodborne and/or sexually–transmitted diseases. A division of studies by virus, including the number of studies for each, total samples collected, number of samples with detectable virus, and fraction of samples with detectable virus are provided in Table B.3. The positivity rates
of the studies, when logit–transformed, were normally–distributed (Shapiro–Wilk test
for normality, W = 0.98, p =0.28). In total, 6804 samples were collected with detectable virus on 1105 for an overall positivity rate of 0.162.
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
83
In total, twelve different eluents (excluding additives) were used in the 74 identified studies. In 7 (9%) of the studies, the authors did not identify the eluent. Table 4.1
provides a summary of the studies, aggregated by eluent type, and includes the number of samples collected, number with detectable virus, and fraction with detectable
virus for each eluent. The authors of thirty–four of the studies (46%) used media,
while 29 (39%) used a saline solution. Studies where eluent was unreported were
grouped into a “not–reported” category and included in the linear model. The linear model demonstrated no signficant influence of eluent category on positivity rate,
when positivity rate was weighted by total samples collected (R2 = 0.05, p =0.33).
Additionally, eluents with calcium were not associated with a significantly different
fraction of samples with detectable virus (p > 0.05).
Four implement types were used in the studies. Studies where implement was
unreported or reported as unspecified swab type (23% of identified studies) were
grouped into a “not reported” category and included in the linear model. A division
of studies by implement type, including the number of studies for each, the total
samples collected, number with detectable virus, and fraction with detectable virus for
each implement type are provided in Table 4.2. Implement type explained 23% of the
variation in positivity rate (R2 = 0.23, p < 0.001) according to the linear model using
logit–transformed positivity rate weighted by total sample number as the dependent
variable. Compared to cotton–tipped swabs, positivity rate was significantly higher
for polyester swabs (p = 0.01) and significantly lower for rayon–tipped swabs (p =
0.02). We found no significant difference between cotton–tipped swabs and antistatic
cloths, although antistatic cloths had the highest positivity rate (0.408), likely due to
the small sample size.
4.4.2
Laboratory–based Surface Sampling Method Comparison
Results of the recovery of MS2 bacteriophage from stainless steel and plastic for each
implement/eluent combination are provided in Table 4.3 and Figure 4.1. The mean
(µ̂) and standard deviation (σ̂) of the fraction recovered from stainless steel was 0.29
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
84
and 0.17, respectively. Recovery from plastic was similar, with a mean and standard
deviation of 0.30 and 0.24, respectively.
As demonstrated by n–way ANOVA (Table 4.4), the surface sampled did not significantly influence the recovery of MS2 bacteriophage (p =0.63). Implement choice,
however, was significant (p < 0.001). Post–hoc Tukey’s test revealed that antistatic
cloths, with overall fraction of recovery of 0.09, were significantly lower than both
polyester swabs (mean recovery fraction = 0.40, p < 0.001) and cotton swabs (mean
recovery fraction = 0.38, p < 0.001). Recovery using polyester and cotton swabs were
not significantly different (p =0.32).
Similarly, eluent significantly influenced fraction of bacteriophage recovered (p
=0.01). The largest fraction was recovered using Ringer’s solution, with a mean
fraction recovered of 0.24, followed by saline (mean recovery fraction = 0.20) and
acid/base (mean recovery fraction = 0.19). Viral transport media recovered the lowest
fraction of virus (mean recovery fraction = 0.17). According to Tukey’s post–hoc test,
the fraction recovered was significantly different only between Ringer’s solution and
viral transport media (p =0.005).
The interaction effect of implement and eluent combination was not significant (p
=0.39). The combination of polyester swab and Ringer’s resulted in the largest mean
fraction recovered (mean recovery fraction = 0.48), though it was not significantly
different (p < 0.05) from polyester and any other eluent or Ringer’s and any other
implement besides antistatic cloth.
4.4.3
qRT–PCR
The surface inoculum, approximately 5×103 PFU, was determined to be approximately 2.9×105 target copies (58 target copies per plaque).
Twenty–eight samples using implement/eluent combinations of cotton/saline and
polyester/Ringer’s were assayed, and five (18%) were removed from analysis because
the quantified target RNA exceeded 100% recovery, or 2.9×105 copies, likely due to
laboratory error as suggested by high standard deviations in the triplicate samples.
Mean and standard deviation of the fraction of target RNA recovered was 0.25 and
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
85
0.22, respectively. No product was detected in the blanks and no template controls
used in the study.
Linear regression of recovered target copies as a function of recovered infective
bacteriophage, surface, and implement/eluent combination demonstrated no significant effect due to surface and sampling method on qRT–PCR recovery. Surface did
not significantly influence recovery using qRT–PCR (p =0.62). Similarly, although
polyester/Ringer’s recovered approximately 18000 greater target copies per sample
than cotton/saline, the difference was not significant (p =0.62).
The linear model also elucidated the relationship between recovery using
qRT–PCR and recovery using a plaque assay.
The linear relationship between
recovered infective bacteriophage and target copies was significant (p < 0.001) and
explained 79% of the variability (R2 = 0.79). Specifically, the ratio of recovered
target copies to infective bacteriophage after a dessication step of 45 ± 1 minute
was 59.8. This is consistent with previous estimates of the ratio of target copies to
infective MS2 bacteriophage as determined using a plaque assay (O’Connell et al.,
2006).
4.5
Discussion
Indoor surface sampling is necessary to understand the role of fomites in disease
transmission. However, studies employ many different sampling methods to recover
virus from surfaces. In the present study, we demonstrate through a combined literature review and laboratory trial, that the sampling method significantly impacts
virus recovery. In fact, sampling method may contribute to the wide range in positivity rates reported across studies. Standardization of a sampling method to the
polyester–tipped swab and 1/4–strength Ringer’s solution or saline solution, may
reduce variability and facilitate cross–study comparisons.
To reduce influence of sampling method on positivity rate, polyester–tipped
swabs should be used for virus detection on surfaces.
Both the meta–analysis
and laboratory trial demonstrated that polyester–tipped swabs improved recovery
relative to other implements. In the meta–analysis the implement choice explained
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
86
24% of the variability in the positivity rate, a strong relationship that suggests that
implement choice affects study outcomes. The recommendation to use polyester
swabs is consistent with the recommendation of the United States Centers for
Disease Control and Prevention to use synthetic fibers for clinical sample collection
(http://www.cdc.gov/h1n1flu/specimencollection.htm).
Cotton–tipped swabs are
known to contain trace contaminants (Ellner and Ellner, 1966; Pollock, 1947) with
demonstrated bacterial inhibition (Pollock, 1947). Similar interference with virus
detection may be possible. Furthermore, the irregular arrangement of cotton fibers
reduces elution of bacteria (Osterblad et al., 2003), and could contribute to the
observed reduced virus recovery relative to polyester. Antistatic cloths recovered the
lowest fraction of seeded virus in the methods comparison study. Antistatic cloths
were not prewetted in this study, which likely contributed to the low recovery. As
antistatic cloth is composed of synthetic fiber, prewetting may provide fractional
recovery similar to polyester–tipped swabs. A potential benefit of using antistatic
cloth is that larger surface areas can be sampled. However, this was not specifically
addressed in our study.
Ringer’s or saline solution should be used as an eluent for virus detection on
surfaces. Although the meta–analysis demonstrated no significant differences in positivity rate attributable to eluent category, the laboratory trial demonstrated that
Ringer’s recovered the greatest fraction of seeded virus using culture–based method,
followed closely by saline solution. The laboratory trial is not meant to be all inclusive
as testing all possible implement and eluent combinations, including additives to the
eluent such as lecithin or Tween 80, is infeasible. However, based on the combinations
tested, Ringer’s or saline solution should be used as eluent in future studies.
Trace calcium in the eluent does not impact virus recovery on surfaces. Calcium
impacts virus adsorption to surfaces (Fuhs et al., 1985) and reduces nucleic acid
detection using PCR by inhibiting the polymerase enzyme (Bickley et al., 2008).
However, based on the meta–analysis, there was no significant influence on positivity
rate attributable to calcium. Similarly, Ringer’s solution (which differs from saline
solution by the addition of potassium and calcium chloride) did not significantly differ
from saline in recovery of infective bacteriophage using plaque assay or target RNA
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
87
using qRT–PCR.
Use of the acid/base eluent method relied on the knowledge of the MS2 bacteriophage isoelectric point (3.9) to aid in recovery. Future studies intending to replicate
this method need to consider the isoelectric point of the virus prior to assay development.
When assessing infection exposure and risk from environmental contamination,
the sampling method’s LLOD is needed (Herzog et al., 2009). Only 13 of the 45
articles reviewed included a quantitative assessment of the LLOD of their sampling
method. The lack of a reported LLOD and the reliance on presence/absence data
makes cross–comparison of studies and relating positivity rates to risk infeasible. In
the present study, the mean and range of fractional recovery of infective bacteriophage
using polyester/Ringer’s was 0.48 and (0.20, 0.98), respectively. Using the mean and
range of fractional recovery, along with the assumption that the bacteriophage double
agar layer method enumerates ≥ 1 PFU, the lower limit of detection is 2.1 with range
(1.0, 5.0) PFU per area sampled. Similarly, assuming the qRT–PCR method has a
lower limit of quantification of ≥ 250 genome equivalents (O’Connell et al., 2006),
consistent with our standard curves, then the theoretical quantification limit is 892
with range (431, 2.5×104 ) genome equivalents based on a mean and range fractional
recovery for RNA of 0.28 and (0.01, 0.58). In the future, reporting the LLOD would
allow authors to combine dose–reponse curves and positivity rates to exposure and
risk estimates (Haas et al., 1999; Julian et al., 2009).
Although a standardized sampling method is recommended to allow cross–comparison
of studies reporting positivity rates, there may be limitations. The recommendation
to use polyester swabs in Ringer’s or saline solution is based on results from both
a laboratory–scale study and a review of literature. The laboratory–scale study
was based on recovery of one virus (MS2 bacteriophage) and two surfaces (high
temperature PVC plastic and type 304 stainless steel with a mirror–like finish).
Pathogenic viruses, however, have wide variation in physicochemical properties (such
as size, shape, and isoelectric point) that may influence recovery by a standardized
method. Similarly, the morphology and composition of the fomites’ surfaces may
also influence recovery. PVC plastic and stainless steel are representative samples
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
88
of many potential fomites, as both are widely used in consumer products (Heudorf
et al., 2007; Adams, 2009). As not all potential fomites are made of PVC plastic or
stainless steel, the method recommended here may not be the most efficient recovery
method for every virus / surface combination sampled. Nevertheless, a standardized
method is recommended for cross–comparison of studies reporting positivity rates.
Our findings suggest polyester–tipped swabs with Ringer’s or saline solution perform
best.
A priority in future research is linking surface contamination to adverse health
outcomes. There is currently limited evidence that virus contamination on fomites
is linked to increased risk of adverse health outcomes. To address this, longitudinal
studies could simultaneously track health outcomes and surface contamination, similar to the work of (Gallimore et al., 2006; Bright et al., 2009; Boxman, Dijkman,
Verhoef, Maat, van Dijk, Vennema and Koopmans, 2009), using the recommended
sampling method. Additionally, quantifying virus concentrations on surfaces is a priority. Knowledge of virus quantity is an important step toward linking fomites to
health risk, as exposures to greater concentrations result in greater risk of infection
(Haas et al., 1999). Sampling surfaces with polyester/Ringer’s or polyester/saline, as
evidenced by this study, is compatible with quantification of virus using plaque assay
or qRT–PCR. Use of a standard method with a known recovery fraction will facilitate
extrapolation of measured surface quantities to exposure and risk estimates.
4.6
Acknowledgments
We thank the members of the Boehm Lab, who assisted with the work and/or provided suggestions for improving the manuscript.
The research has been funded, in part, by the UPS Foundation and the United
States Environmental Protection Agency (EPA) under the Science to Achieve Results
(STAR) Graduate Fellowship Program, Assistance Agreement No. F07D30757. FT
was supported by NSF awards BES–0641406 and SES–0827384. EPA has not officially
endorsed this publication and the views expressed herein may not reflect the views of
the EPA.
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
4.7
Figures
89
Fraction Recovered
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
1.0
0.8
0.6
0.4
0.2
0.0
Acid/Base
Ringer's
90
Saline
VTM
●
●
●
●
●
●
●
●
●
A
C
P
A
C
P
A
C
P
A
C
P
Figure 4.1: Fraction of seeded MS2 bacteriophage recovered by implement/eluent
combination using the double agar layer method to enumerate plaque forming units.
Abbreviations used include: “VTM” for viral transport medium, “A” for antistatic
cloth, “C” for cotton, and “P” for polyester–tipped swabs. Boxes depict the 25th, median, and 75th quartiles. Whiskers represent the 10th and 90th percentiles. Outliers
are denoted by “•”
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
4.8
Tables
91
3
2
2
3
29
18
9
2
4
7
74
MEMc
RPMI1640c
LB
Amiesc
Saline Solutions
Saline
PBS
Ringer’sc
Water
Not Reported
Total
6804
120
278
125
420
538
99
218
123
2763
2218
1418
No.
Samp.
3643
730
93
424
1105
11
98
51
88
37
29
83
33
367
228
332
No. Pos.
Samp.
629
12
3
100
0.162
0.092
0.353
0.408
0.210
0.069
0.293
0.381
0.268
0.133
0.103
0.234
Frac.
Pos.
0.173
0.016
0.032
0.236
Gallimore et al. (2006, 2008); Wu et al. (2005); Piazza et al. (1987); Froio
et al. (2003); Boone and Gerba (2005); Ferenczy et al. (1989); Lederman
et al. (2009); Bellamy et al. (1998); Lessa et al. (2009)
Gallimore et al. (2005); Strauss et al. (2002); Butz et al. (1993); Kawahara
and Yoshida (2009); Kuusi et al. (2002); Widdowson et al. (2002); Bausch
et al. (2007); Lopez et al. (2008)
Boxman, Dijkman, Verhoef, Maat, van Dijk, Vennema and Koopmans
(2009); Boxman, Dijkman, te Loeke, Hagele, Tilburg, Vennema and Koopmans (2009)
Runner (2007)
Girou et al. (2008); Lyman et al. (2009); Hamada et al. (2008); CDC
(2008)
Akhter et al. (1995)
Fischer et al. (2008); Carducci et al. (2002)
Wilde et al. (1992); Winther et al. (2007); Pappas et al. (2009); Gwaltney
(1982)
Cheesbrough et al. (2000); Green et al. (1998); Goldhammer et al. (2006);
Chen et al. (2004); Booth et al. (2005); Russell et al. (2006); Dowell et al.
(2004)
Keswick et al. (1983); Gurley et al. (2007); Soule et al. (1999)
Asano et al. (1999); Yoshikawa et al. (2001)
Boone and Gerba (2005)
Jones et al. (2007); Bright et al. (2009)
Ref.
Table 4.1: Summary of the eluents used in the reviewed articles, including number of studies (“No. Studies.”),
number of samples collected (“No. Samp.”), number of samples with detectable virus (“No. Pos. Samp.”), and
fraction of samples with detectable virus (“Frac. Pos.”). Eluents with trace calcium are denoted by ’c ’
9
No.
Studies
34
5
2
8
VTM
Eluent
Media
TPB gel
BE
BHIB
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
92
12
4
2
17
74
Polyester
Rayon
Antistatic
Not Reported
Total
6804
1165
571
125
1411
No.
Samp.
3532
1105
128
36
51
388
No. Pos.
Samp.
502
0.162
0.110
0.063
0.408
0.275
Frac.
Pos.
0.142
Ref.
Cheesbrough et al. (2000); Gallimore et al. (2005, 2006,
2008); Green et al. (1998); Keswick et al. (1983); Wilde et al.
(1992); Winther et al. (2007); Gurley et al. (2007); Goldhammer et al. (2006); Wu et al. (2005); Chen et al. (2004);
Strauss et al. (2002); Pappas et al. (2009); Asano et al.
(1999); Butz et al. (1993); Fischer et al. (2008); Gwaltney
(1982); Kawahara and Yoshida (2009); Kuusi et al. (2002);
Soule et al. (1999); Widdowson et al. (2002); Piazza et al.
(1987); Yoshikawa et al. (2001); Carducci et al. (2002); Froio
et al. (2003)
Bausch et al. (2007); Boone and Gerba (2005); Dowell et al.
(2004); Ferenczy et al. (1989); Lederman et al. (2009); Lopez
et al. (2008); Russell et al. (2006)
Bellamy et al. (1998); Bright et al. (2009); Jones et al. (2007)
Boxman, Dijkman, Verhoef, Maat, van Dijk, Vennema and
Koopmans (2009); Boxman, Dijkman, te Loeke, Hagele,
Tilburg, Vennema and Koopmans (2009)
Akhter et al. (1995); Girou et al. (2008); Hamada et al.
(2008); Lessa et al. (2009); Lyman et al. (2009); Runner
(2007)
Table 4.2: Summary of the implement types used in the reviewed articles, including the number of studies (“No.
Studies.”), the total number of samples (“No. Samp.”), the samples with detectable virus (“No. Pos. Samp.”),
and the fraction of samples with detectable virus (“Frac. Pos.”) are also provided
No.
Studies
39
Implement
Cotton
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
93
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
Stainless Steel
µ̂
median
σ̂
Implement Eluent
Cotton
Saline
0.38
0.38
0.15
0.33
0.34
0.11
Ringer’s
VTM
0.35
0.38
0.13
0.32
0.12
Acid/Base 0.32
Polyester
Saline
0.39
0.38
0.17
0.39
0.38
0.13
Ringer’s
VTM
0.29
0.30
0.13
0.38
0.17
Acid/Base 0.39
Antistatic Saline
0.15
0.13
0.15
0.16
0.10
0.18
Ringer’s
0.10
0.07
0.12
VTM
Acid/Base 0.23
0.24
0.14
µ̂
0.39
0.54
0.36
0.37
0.39
0.59
0.39
0.48
0.007
0.032
0.009
0.003
94
Plastic
median
σ̂
0.45
0.16
0.56
0.16
0.34
0.07
0.33
0.15
0.41
0.12
0.56
0.21
0.37
0.13
0.48
0.11
0.003
0.01
0.003
0.08
0.003
0.01
0.003 0.001
Table 4.3: Summary of the fraction of MS2 bacteriophage recovered using each implement/eluent combination from stainless steel and plastic surfaces. The mean, median,
and standard deviation are reported
CHAPTER 4. VIRUS RECOVERY FROM SURFACES
Effects
Surface
Implement
Eluent
Implement:Eluent
Residuals
d.f.
1
2
3
6
217
Sum of Squares
0.01
4.76
0.24
0.13
4.6
Mean Square
0.01
2.38
0.08
0.02
0.02
95
F–value
0.23
111.5
3.73
1.05
p–value
0.63
<0.001
0.01
0.40
Table 4.4: Fraction of virus recovered from a seeded surface as a function of the
surface’s material and the implement and eluent used, based on statistical results of
n-way ANOVA
Chapter 5
Evidence for Causal Links between
Respiratory Illness and Indicator
Bacteria on Surfaces in Child Care
Centers
.
The results presented in this chapter will be submitted to a peer reviewed journal
in Winter 2011. Amy J. Pickering contributed extensively to the experimental design,
data collection, data analysis, and manuscript preparation, and will be co-author
on the resulting publication. James O. Leckie and Alexandria B. Boehm will also
appear as co-authors, for their contributions to study design, data interpretation,
and manuscript improvements.
96
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
5.1
97
Abstract
BACKGROUND: The link between microbial contamination on surfaces and
health outcomes has not been fully established. Investigating temporal trends in
health and environmental contamination may provide evidence for causal links
between surface contamination and adverse health outcomes.
OBJECTIVES: The objective is to investigate causal relationships between contamination on hands and surfaces and health in child care centers.
METHODS: The present study tracks both respiratory and gastrointestinal disease incidence while monitoring weekly hand and environmental surface contamination over four months in child care centers. Microbial contamination was determined
using quantitative densities of fecal indicator bacteria as well as presence/absence of
viral pathogens. Health was monitored daily by childcare staff, who tracked adverse
health outcomes, including respiratory illness.
RESULTS: Symptomatic respiratory illness is significantly and positively associated with hand contamination and with environmental contamination. Detection of
enterovirus on hands provides further support of the importance of surfaces in disease
transmission.
CONCLUSIONS: Symptomatic respiratory illness is both caused by, and causes
increases in microbial contamination on hands. Specifically, increases in microbial
contamination led to increases in symptomatic respiratory illness four to six days
later, in agreement with typical incubation periods for respiratory illness. Respiratory
illness also led to increases in microbial contamination on hands during presentation
of symptoms.
5.2
Introduction
Over 4.6 million children under the age of five years old are enrolled in center-based
child care in the United States (Laughlin, 2010). Children attending center-based
child care suffer 1.5-3 times more respiratory and 2-3.5 times more gastrointestinal
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
98
episodes per year than those in home-based child care (Fleming et al., 1987; Alexander et al., 1990; Lu et al., 2004). Attendees of center-based care are not the only
ones with increased risk of illness. Evidence of the role of children in disseminating
disease through communities abounds. Examples include the documented persistence
of hepatitis A infections in areas with child care centers (Hadler et al., 1980; Desenclos and MacLafferty, 1993), increased disease prevalence in households with children
attending child care (Garrett et al., 2006), and reductions in prevalence within communities following the vaccination of children (Dagan et al., 2005; Loeb et al., 2009).
Therefore, reductions in infectious disease transmission within child care centers may
influence burden in the community-at-large.
Reductions in disease burden are often achieved through interventions tailored to
interrupt known transmission routes. Studies have shown that in child care centers,
the introduction of hygiene programs significantly reduce gastrointestinal disease by
interrupting direct and indirect contact transmission (Krilov et al., 1996; Roberts,
Jorm, Patel, Smith, Douglas and McGilchrist, 2000; Lennell et al., 2008; Sandora
et al., 2008). However, the efficacy of hygiene programs in reducing respiratory illness is less certain. Hygiene intervention studies report conflicting results (Roberts,
Smith, Jorm, Patel, Douglas and McGilchrist, 2000; Sandora et al., 2008) despite the
documented importance of contact transmission for common respiratory pathogens
like rhinovirus and respiratory syncytial virus (Gwaltney et al., 1978; Hall et al.,
1980). Nevertheless, the success of hygiene programs is perceived to result from a reduction of the role of surfaces (e.g. hands and fomites) as mediators in transmission
(Lennell et al., 2008). In support of this perception, multiple studies have demonstrated presence of pathogenic agents on hands and fomites during disease outbreaks,
and suggest that the presence of agents is an indicator of infection risk (Green et al.,
1998; Cheesbrough et al., 2000; Boone and Gerba, 2005; Wu et al., 2005).
In fact, the link between microbial contamination on surfaces and health outcomes
has not been fully established. Indoor environmental contamination may be endemic,
or it may be an outcome from existing infectious disease while not contributing to
further transmission. Among the first studies to establish a relationship between microbial contamination on surfaces and diarrheal illness is the work by Laborde et al.
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
99
(1993), which showed a significant association of hand and surface contamination
with reported diarrheal illness in 37 child care centers. Contamination was quantified
during a single survey for fecal coliform and was used as a representative sample for
the child care center. Conversely, a similar study by Soule et al. (1999) focusing on
rotavirus gastroenteritis in pediatric wards found no significant difference in rotavirus
on surfaces when comparing rooms occupied by patients with symptomatic rotavirus
gastroenteritis to those of uninfected patients. Although the cross–sectional analyses demonstrate potential relationships between environmental contamination and
health, they can not elucidate causal links.
Previous research has provided limited evidence of causal links between environmental contamination and adverse health outcomes. For example, laboratory studies
have demonstrated that infected individuals handling fomites increase rhinovirus presence (Gwaltney, 1982; Winther et al., 2007). Additionally, in child care centers, Butz
et al. (1993) demonstrated that presence of rotavirus contamination on surfaces followed two of five observed diarrheal outbreaks. However, none of the aforementioned
studies investigated the role of surface contamination in precipitating outbreaks, as all
investigated increases in fomite contamination after illness. In two other studies (Van
et al. (1991) and Bright et al. (2009)), the authors tracked health and the presence of
organisms on fomites and hands in child care centers, but did not incorporate temporal lags in analyses. Therefore, the studies acted more as cross–sectional analyses.
Neither study found significant associations between illness and surface contamination (Van et al., 1991; Bright et al., 2009), although Van et al. (1991) supported
findings that bacteria on hands is linked to diarrheal illness.
In the present study, we investigated the temporal relationship between health
and contamination of fomites and hands. The study is the first, to our knowledge, to
track both respiratory and gastrointestinal disease incidence while monitoring weekly
hand and environmental surface contamination. Microbial contamination in two child
care centers was determined using quantitative densities of fecal indicator bacteria
(e.g. Escherichia coli, enterococci, and fecal coliform) on hands and fomites as well
as presence/absence of viral pathogens (e.g. enterovirus and norovirus). Health was
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
100
monitored daily by childcare staff, who tracked absenteeism, illness-related absenteeism, and symptomatic respiratory and gastrointestinal illness. Based on the data
set, we investigate relationships between increases in symptomatic respiratory illness
and increases in microbial contamination on hands. By incorporating temporal lags,
we assess whether increases in hand or environmental contamination levels result
in, or are caused by, increases in respiratory illness when accounting for incubation
periods typical for common respiratory illness.
5.3
5.3.1
Methods and Materials
Sites
Permission from the Stanford University Research Compliance Office for Human Subjects Research was obtained prior to the study. 80 individuals were enrolled in the
study at two child care centers in Northern California, USA: 8 child care center staff
(100% female) and 72 children (36% female) aged 3-5 years. Hereafter, the sites
are referred to as sites A and B. Each of the child care centers has a morning class
from 8:00-11:30 with 17-20 enrolled children and a separate afternoon class from
12:30–16:00 with 13-17 enrolled children. The children are assigned one of the class
times, and did not change times. Three staff members are on site at each facility.
Child care staff or the children’s parents/guardians provided written consent at the
start of the study. The child care centers were chosen because of similarities in: 1)
geographic location, 2) admission requirements, 3) class schedule, 4) enrollment size,
5) facility layouts, 6) staff size, 7) janitorial service, and 8) food vendor. Additionally,
the cleaning and hygiene regimens at the two centers were similar. The same janitorial service cleaned each facility nightly. Children were encouraged to wash their
hands with soap and water upon arrival at the facility, following breakfast or lunch
(at 8:45 or 13:15) and before snack (at 10:45 or 15:30). At Site B, staff encouraged
children to use alcohol based hand sanitizer (ABHS) in addition to soap and water.
Specifically, children upon arrival, before breakfast and lunch, before playtime, and
when coming in from the outside.
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
5.3.2
101
Surveys / Demographic Data Collection
In-person or telephone surveys lasting approximately 10-15 minutes were given to the
child care center staff and the parents or guardians of the enrolled children to obtain
information on study population demographics. The survey also included questions
on hygiene habits (e.g., frequency of handwashing for staff or parent and child), and
perceived health of the child such as previous gastrointestinal and respiratory illness,
likelihood of future illness, and perception of overall health as rated on a scale of
1-10, with 10 being extremely healthy (referred to as the “healthy child index”). The
survey was given at the beginning of the study; a shorter follow-up survey lasting
approximately 5-10 minutes including questions on hygiene habits, knowledge, and
perceived health of the child was administered at the end of the study as well.
5.3.3
Sampling Scheme
Between 5 February 2009, and 1 June 2009, the morning and afternoon classes of
both child care centers were visited by the research team weekly. A total of 64
sample events occurred over 16 weeks. Each visit lasted approximately one hour,
typically starting between 8:30-10:30 for the morning class and 13:00-15:00 for the
afternoon class. The visits were scheduled so as not to interrupt the children’s snacks
or learning activities. During each visit, 2-3 research team members collected 8-12
hand rinse samples, collected 5 environmental surface samples, and verified that the
health chart (see below) was filled out appropriately.
5.3.4
Health Data Collection
Attendance and symptoms of infections among the children and staff were recorded,
daily, by the child care center staff. The staff used standardized health charts that
included check boxes for symptoms of respiratory and gastrointestinal illness including
“stomach pain”, “3 or more bowel movements”, “vomit”, “bloody stool”, “diarrhea”,
“runny/stuffy nose”, “fever”, “sore throat”, “cough” and “headache” as well as a
comments section to allow for elaboration on reasons for absenteeism or descriptions of
“other” symptoms. The staff were not aware of any of the microbiological laboratory
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
102
results until the end of the study. The research team examined completion of the
health charts during sampling trips twice weekly, and collected the charts on the first
sampling trip of the following week.
Individuals with no recorded symptoms on a given day were classified as “nonsymptomatic”, otherwise the individual was classified as “symptomatic”. Symptomatic illness was further classified as “respiratory” (“runny nose”, “headache”,
“cough”, or “sore throat”) or “gastrointestinal” (“stomach pain”, “diarrhea”, “bloody
stool”, “more than 3 bowel movements”, or “recent vomiting”) illness. Consecutive
days of symptomatic illness were classified as new episodes if they were preceded by
six symptom-free days, similar to the description of new episodes of illness described
elsewhere (Payment et al., 1991; Colford Jr et al., 2002). New episodes are hereafter
referred to as “new illness episodes” and are described by the first day of symptomatic
illness. The first day of illness was counted as the first observation of symptoms by
child care staff or a reason provided by parents for an absence.
The duration of an episode was calculated as the number of consecutive days
with reported symptoms or illness-related absenteeism. As no data were collected
on weekends, an episode was assumed to end on a Friday if no symptoms or illnessrelated absenteeism were reported on the following Monday, whereas if symptoms or
illness-related absenteeism were reported on the following Monday, the episode was
assumed to include the weekend.
5.3.5
Hand Rinse Sampling
Between 8 and 12 hand rinse samples were collected during each visit, for a total
of 616 samples over the duration of the study. Eight to ten children were sampled
during each visit, and the three child care staff at each facility were sampled once
per week. The children were asked to participate in an order determined through
randomization of the list of enrolled children. If a child declined, the next child on
the list was approached. Once assent was obtained, the researcher recorded whether or
not there were visible signs of a runny nose, dirt on hands, and dirt under fingernails.
The researcher then asked the subject how s/he was feeling, and the response was
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
103
recorded and later reclassified as “sick”,“fine”, or “no response”. Hand rinse sampling
was performed using a modified glove-juice method as previously described (Pickering
et al., 2010). The subject was asked to place first one hand, and then the other, into
the same 69 oz. Whirl-pak bag (Nasco, Fort Atkinson, WI, USA) filled with 350
ml autoclaved Milli-Q grade water. The subject was encouraged to shake the hand
vigorously for 15 s; the researcher then massaged the hand through the bag for an
additional 15 s. After both hands were rinsed, the subject was provided a clean paper
towel to dry her/his hands. The sample was placed on ice and transported to the
laboratory, where it was processed within 6 hours.
5.3.6
Environmental Surface Sampling
Surface samples were obtained immediately after the hand rinse samples. Between
three and five fomites were sampled during each visit, for a total of 299, chosen based
on the subjective classification as a surface with a high likelihood of contact based on
children’s activities in the previous hour. For example, a toy block would be sampled
if one or more children had been observed playing with the block. A summary of the
fomites tested is reported in Table 5.1, with the most commonly sampled surfaces
including toys, table tops, faucets, and doorknobs. The surface was sampled with
a sterile cotton-tipped applicator wetted in 10 ml of 1/4 strength Ringer’s solution.
The area sampled varied between approximately 25-100 cm2 , depending on the object
tested. After sampling, the swab was replaced in the 1/4-strength Ringer’s solution
and transported to the lab for bacterial assay (Kaltenthaler et al., 1995; Kyriacou
et al., 2009).
5.3.7
Microbiological Methods
Bacterial Assays
All hand and surface samples were assayed for three fecal indicator bacteria: fecal
coliform, enterococci and Escherchia coli. Membrane filtration was used to enumerate
the bacteria. Fecal coliform was grown on mFC agar (BD Diagnostics, Inc, USA)
at 44.5◦ C according to the U.S. EPA standard methods for water quality testing
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
104
(Bordner et al., 1978). Enterococci was grown on mEI agar (BD Diagnostics, Inc,
USA) at 41.5◦ according to U.S. EPA Method 1600 (USEPA, 2002a). Escherchia coli
was grown on modified mTEC agar (BD Diagnostics, Inc, USA) according to U.S.
EPA Method 1603 (USEPA, 2002b) with incubation at 35◦ for two hours followed
by incubation at 44.5◦ for an additional 22 hours. For the hand rinse samples, a
volume of between 65-80 ml of the total 350 ml collected was filtered. For the surface
samples, a volume of between 2-2.5 ml of the total 10 ml collected was filtered. To
calculate bacterial concentration per two hands, the colony counts were multiplied
by the ratio of total sample volume collected (350 ml) to sample volume filtered for
each sample. For data analysis of hand rinse samples, the lower detection limit is 5.4
CFU per two hands, based on a 65 ml filtered sample volume. When no detectable
bacteria was present, 1/2 the lower limit of detection (2.7 CFU per two hands) was
used. For data analysis of surface samples, the lower detection limit is 5 CFU per
surface, based on a 2 ml filtered sample volume. Surfaces samples were classified as
either contaminated (≥5 CFU per 25-100 cm2 surface) or uncontaminated when no
bacteria were detectable.
Viral Assays
A subset of sixty-seven hand rinse samples were also tested for the presence of enterovirus, norovirus genogroup I, and norovirus genogroup II. The sample volume
remaining after bacterial assays, between 80-110 ml, was filtered through a 0.45um
negatively-charged nitrocellulose filter (HA filter, Millipore, Billerica, MA, USA),
placed in a 2 oz Whirl-pak bag (NASCO Corp., Fort Atkinson, WI) and stored at
-80◦ . Both RNA and DNA were extracted using the Qiagen AllPrep DNA/RNA Mini
Kit (Qiagen, Valencia, CA, USA) and eluted in 60 µl of RNAse/DNAse free water.
RNAse/DNAse free water was used as an extraction blank with every set of ten samples extracted. 5 µl of template was then used in a 25 µl RT-PCR reaction, the details
of which are included in Table 5.2. RT-PCR reagents used were the Qiagen One-Step
RT-PCR kit (Qiagen, Valencia, CA, USA). Thermocycler conditions were obtained
from RT-PCR kit manufacturer’s recommendations using the annealing temperatures
listed in Table 5.2, and optimized on an Applied Biosystems Thermal Cycler 9700
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
105
(Applied Biosystems, Foster City, CA). All PCR products were visualized on 1.5%
agarose gels using a BioRad Gel Doc XR system (BioRad, Hercules, CA).
5.3.8
Statistics
Most statistics were performed using PASW Statistics 18.0.2. (SPSS: An IBM Company, Chicago, IL, USA). Statistical methods are reported with the results, with
additional details available in Appendix C. Analyses using generalized estimating
equations (GEEs) were performed using the “geeglm” function in the “geepack” package in R (version 2.11.1, R Foundation for Statistical Computing, Vienna, Austria)
(Zuur et al., 2009). Magnitude of the coefficients (β) and significance level (p) are
reported, where the significance level used throughout the study is α =0.05.
5.4
Results
5.4.1
Surveys
The population characteristics are presented in Table 5.3, including age of the parent/guardian respondent, ethnicity of the child, and percent of children with a chronic
disease and taking medication. The only category in which the parents of children attending Site A and Site B differed significantly (p = 0.02) was the number of residents
under 6 years old in their households. The parents of children at Site A had a mean
0.5 more residents under six years old than parents at Site B. Characteristics of hand
hygiene habits, general health of the child, and previous and predicted respiratory
and gastrointestinal illness are reported in Table 5.4.
5.4.2
Health Data
A total of 5,651 person-days of health data were collected over the duration of the
study. Attendance and symptomatic illness status were recorded for 5,619 (99.4%)
and 5,503 (97.4%) days, respectively. The days without recorded attendance or symptomatic illness data were excluded from analysis.
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
106
Summaries of attendance and symptomatic illness for staff and children at each
site are presented in Table 5.5. In total, children and staff were absent on 636 (11.3%)
person-days. Symptomatic illness in children or staff was observed and reported by
child care center staff on 1063 person-days, or 19.3%) of the total 5,303 person-days
with recorded symptomatic illness status. On an additional 94 (1.7%) person-days,
children were absent due to illness with symptoms that were not specified by the
parents or guardians; these person-days were included in analyses of symptomatic
illness but treated as missing data in analyses of respiratory or gastrointestinal illness.
A time series of the absences and illness-related absences of children and staff at both
of the centers is shown in Figure 5.1. Similarly, times series of respiratory illness,
gastrointestinal illness, and new illness episodes are shown in Figure 5.2.
Of the 5,503 person-days with recorded symptomatic illness status, respiratory
symptoms were reported on 18.3% (or 1,010 person-days). The most common respiratory symptoms were runny nose (15.8% or 872 of the 5,503 person-days), cough
(4.8% or 265 person-days), and sore throat (0.9% or 52 person-days). Gastrointestinal symptoms were reported on 38 (0.7%) person-days. The most common gastrointestinal symptoms were vomiting (0.3% or 18 person-days), stomach pain (0.3% or 18
person-days), and diarrhea (0.1% or 5 person-days). Fever was reported on 83 persondays (1.5%), 46 (0.8%) person-days were in conjunction with respiratory symptoms,
12 (0.2%) person-days with gastrointestinal symptoms, and 25 (0.5%) person-days as
the only symptom.
A total of 232 new illness episodes were identified during the study. Of those, 118
(50.8%) new illness episodes were first identified and reported by child care center
staff as symptomatic illness on a day when the child or staff member was present. The
remaining 114 (49.1%) new illness episodes were first reported as an absenteeism due
to illness. The majority of the new illness episodes were respiratory (161 episodes, or
69.4%). The rest were gastrointestinal (15 episodes, or 6.4%), fever alone (14 episodes,
or 6.0%), or an illness-related absences with unspecified symptoms (43 episodes, or
18.5%). The duration of episodes ranged between 1 and 48 days, with a mean and
median of 6.7 and 3 days, respectively.
Rates of health outcomes varied by site, as compared using Pearson’s χ2 . Site A
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
107
had significantly fewer absences (p = 0.02), illness-related absences (p = 0.001), and
respiratory illness (p < 0.001) than Site B. There was no significant difference by site
in gastrointestinal illness (p = 0.20) or new illness episodes (p = 0.82). Because of the
significant differences in absences, illness-related absences, and respiratory-illness, all
analyses of health outcomes using GEEs included a site-dependent variable.
5.4.3
Hand Rinse Samples
Of the 616 hand rinse samples collected, enterococci, fecal coliform, and E. coli were
detected in 208 (33%), 83 (14%), and 31 (5%), respectively. The range in concentrations of bacteria on hands was the same for all three bacteria: 8–≥1000 CFU per 2
hands. A time series of the fraction of hand rinse samples with detectable enterococci
and fecal coliform is presented in Figure 5.3. Norovirus gI and gII were not detected
in any of the 67 hand rinse samples tested. Enterovirus was detected in four of the
67 hand rinse samples tested (6%).
Mean concentrations of all three fecal indicator bacteria were higher on the hands
with visible dirt, visible dirt under nails, and on volunteers with visible runny noses.
The concentrations of E. coli (p = 0.022), enterococci (p = 0.003), and fecal coliform
(p = 0.006) were significantly higher when dirt on hands was visible, with mean effect
sizes of 0.052, 0.113, and 0.141 log10 CFU per two hands, respectively. Only the
concentration of fecal coliform (p = 0.035), but not E. coli (p = 0.206) or enterococci
(p = 0.239), was significantly higher when dirt under nails was visible with a mean
effect size of 0.077 log10 CFU per two hands. A visible runny nose was associated with
significantly higher concentrations of enterococci (p = 0.001) and fecal coliform (p =
0.021), with mean effect sizes of 0.240 and 0.095 log10 CFU per two hands, respectively,
but not with concentrations of E. coli (p = 0.089). A volunteer’s response on how
s/he was feeling at the time of the sampling was not associated with the concentration
of E. coli (p = 0.459), enterococci (p = 0.100), or fecal coliform (p = 0.511) on the
hands. The low prevalence of enterovirus on hands precluded statistical analysis
with presentation of symptoms or presence of fecal indicator bacteria. Two of the
four children with detectable enterovirus also had reported symptomatic respiratory
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
108
illness.
5.4.4
Environmental Samples
In total, 299 environmental samples were collected. A summary of the objects tested
and the number of samples with detectable enterococci and fecal coliforms are presented in Table 5.1. Enterococci and fecal coliform were detected in 19 (6%) and
9 (3%) of the samples, respectively. E. coli were not detected in any of the samples. Concentrations on fomites for both enterococci and fecal coliform ranged from
≤5–≥1000 CFU per 100 cm2 .
The presence of enterococci on a surface was significantly correlated to the presence of fecal coliform (McNemar χ2 test, p = 0.041). The presence of enterococci
or fecal coliform was not significantly associated with site (Pearson χ2 test, enterococci p = 0.59, fecal coliform p = 0.82) or time of class (Pearson χ2 test, enterococci
p = 0.89, fecal coliform p = 0.79). Data on the fraction of fomites sampled in a
given week with detectable enterococci or fecal coliform were used in all time series
analyses, as presented in Figure 5.4.
5.4.5
Health Associations with Hand and Surface Contamination
Fifteen separate GEE were used to examine the associations between respiratory
illness and microbial contamination on hands and surfaces for daily lags of up to
-7 through +7 days (Table 5.6). Data were clustered by child, and a variable was
included to control for site. Symptomatic respiratory illness is significantly and positively associated with hand contamination on the same day (β = 0.40, p = 0.003),
one day before (β = 0.31, p = 0.041)) and one day later (β = 0.41, p = 0.005), four
days before (β = 0.99, p =< 0.001), and seven days later (β = 0.41, p = 0.015). In
addition, there is a significant and positive association with environmental contamination as measured four (β = 1.37, p = 0.42) and five (β = 0.79, p = 0.014) days
before as well as two (β = 0.69, p = 0.023) and three (β = 0.091, p = 0.030) days
later.
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
109
No significant associations between hand and environmental contamination with
either illness-related absences or new episodes of illness were found (See Tables C.1
and C.2). Gastrointestinal illness prevalence was low, and therefore insufficient to
analyze associations between gastroenteritis and microbial contamination in a manner
similar to respiratory illness.
5.5
Discussion
In child care centers, hygiene plays an important role in reducing transmission of
both gastrointestinal and respiratory illness. Previous field studies in child care centers have demonstrated significant correlations between microbial contamination and
adverse health outcomes (Van et al., 1991; Laborde et al., 1993), but this study is
the first to our knowledge to infer causality based on analysis of temporal trends.
As such, the study provides insight into the timing of microbial contamination relative to symptomatic illness. Specifically, increases in detectable enterococci on hands
and fomites precedes symptomatic respiratory illness by a four- to six- day period
consistent with incubation periods for respiratory diseases (Long et al., 1997). Furthermore, the study demonstrates that the occurrence of enterococci on hands and
fomites increases in the two days following symptoms. These findings suggest that
respiratory illness can contribute to, and result from, microbial contamination on
hands and fomites.
The illness rates and microbial contamination in this study used to infer causal
relationships are consistent with previously observed values. The estimated rates for
total illness (0.76 per child per month) and respiratory illness (0.63 per child per
month) are similar to the rates reported for children of similar ages attending child
care (Wald et al., 1991; Dahl et al., 1991; Krilov et al., 1996). Microbial contamination is also similar to previous studies. In classrooms with children from infancy
to under five years old, fecal coliform and fecal streptococci (a group of organisms of
which enterococci are a subset) were observed on 4-10% and 16% of fomites sampled
(Weniger et al., 1983; Holaday et al., 1990; Kyriacou et al., 2009), consistent and
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
110
expectedly greater than the 3% and 6% detection rates in the present study. Similarly, our detection rates for fecal coliform (14%) and enterococci (33%) on hands are
consistent with reported detection rate of 6-20% for fecal coliform (Van et al., 1991;
Holaday et al., 1990), and 52.9% for fecal streptococci (Kyriacou et al., 2009).
Respiratory symptoms increase with enterococci occurrence on hands on the same
day, one day before, and one day after. Although other studies have demonstrated
significant correlations between bacteria on hands and health (Van et al., 1991; Pickering et al., 2010), this is the first study to demonstrate significant associations with
daily lags. This finding suggests enterococci acts as a superior indicator for respiratory illness relative to both fecal coliform or E. coli. Enterococci, while commonly
isolated in feces, have also been isolated from the mouth (Murray, 1990) and the nose
(Crossley and Ross, 1985). In the present study, runny/stuffy nose or coughing were
reported as the majority of symptoms, providing a possible source of enterococci to
the environment. In support, individuals with visible runny noses had significantly
higher concentrations of enterococci on hands. Furthermore, (Pickering et al., 2011)
demonstrated, in Africa, significant associations between enterococci density on mothers’ hands and time since last handwashing. The same relationship was not significant
for E. coli (Pickering et al., 2011).
Respiratory symptoms significantly lag enterococci occurrence on fomites two to
three days later. This finding is consistent with asymptomatic excretion of microorganisms persisting after the conclusion of symptoms, as has been reported for respiratory viruses (Long et al., 1997). However, the median duration of symptomatic
illness is three days, so significant associations within three days of symptoms may
not necessarily imply associations occurred in the absence of symptoms. Similarly,
the majority of fomites with detectable enterococci were toys that were not cleaned
regularly by the nightly janitorial staff. Significant associations, therefore, may be
a result of enterococci persistence. Pinfold (1990) suggests that fecal streptococci, a
group of microorganisms that include enterococci, are more likely to survive crosscontamination than E. coli because enterococci survival on fingertips is 4-8 times
longer (Pinfold, 1990). Nevertheless, the study suggests that hygiene interventions
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
111
targeting recovered individuals for up to three days following the conclusion of observable symptoms may reduce illness transmission.
Enterococci occurrence on fomites preceded increases in respiratory illness by four
to five days. As the incubation period for common respiratory viruses is 2-5 days
(Long et al., 1997), this finding is among the first field evidence of a causal role
of microbial contamination on fomites in respiratory disease transmission. As enterococci may be shed with nasal secretions, the findings suggest that the increased
presence on fomites may be indicative of increased presence of pathogens responsible
for outbreaks, as well.
The detection of enterovirus on hand rinse samples demonstrates the potential role
of hands in pathogen transmission. The pan-enterovirus primers used in this study to
detect enterovirus are capable of detecting some serotypes of rhinovirus, a common
respiratory virus in child care centers (Rotbart, 1995). The low detection rate (6%
of hand rinse samples tested) is consistent with a previous study conducted in Africa
(Pickering et al., 2011). A higher detection rate may have been possible with a more
efficient virus recovery method. We compared direct extraction of sewage to the hand
rinse sample method by spiking hand rinse water with sewage and found an approximate ten–fold increase in the lower limit of detection (data not shown). Accounting
for the ten–fold increase, PCR template volume, hand rinse sample volume, and a
PCR reaction lower limit of detection equal to the published 2.5 PFU of poliovirus
(Jaykus et al., 1996), the detection limit for virus is at least 1500 PFU poliovirus per
two hands. Efforts to reduce the detection limit may yield higher pathogen detection
rates.
The only health outcomes demonstrating significant associations with microbial
contamination were respiratory symptoms. The other health outcomes modeled,
illness–related absences and new illness episodes, did not demonstrate significant
trends when modeled as a function of enterococci contamination on surfaces (See
Appendix C). Modeling absences, including illness-related absences, as a function of
microbial contamination was confounded by the inability to collect data for children
who are not present during sampling trips. The lack of data on microbial contamination on hands of absent children likely contributed to bias in the illness-related
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
112
absences model. New illness episodes are similarly impacted as absences accounted
for almost half (49%) of all defined illnesses. In fact, symptomatic respiratory illness
is well–suited to be modeled as a function of microbial contamination as shedding,
particularly during symptomatic illness, is perceived to be a cause of transmission
and contamination (Hall et al., 1980). For a further discussion of the health outcome
model used, specifically the use of multiple comparisons, refer to the Appendix C.
The present study suggests that respiratory illness both lags and leads increased
environmental surface and hand contamination. However, data collection methods
and statistical analysis used in the study may bias the findings. Symptomatic illness
in this study, as it was reported by child care center staff, is a subjective measure.
Evidence of the influence of subjectivity of the child care center staff includes the
significantly different respiratory incidence rates between Site A and Site B, despite
similarity between the two centers (See Table 5.3) and a similar number of new
episodes of illness (See Table 5.5). At the end of the study the child care center
staff were prompted on the recording frequency, and the resulting quality of the child
health charts. Between the choices of “highly”, “somewhat”, and “not accurate”,
the child care center staff reported that the quality of the data was “somewhat accurate” and that the charts were filled out daily. Identification of health outcomes,
specifically the presentation of symptoms, by trained professionals combined with
collection and analysis of clinical samples for specific etiological agents would likely
improve reliability of health measurements. Similarly, health data were only collected
on weekdays. Therefore, samples collected on days where the associated lag time corresponded to a weekend were not included in the analysis. However, the frequency
of illness relative to the number of observations for every lag remained consistent
(See Table 5.6) suggesting bias may be minimized. Finally, as the study covered only
four months, seasonal trends in illness may be confounded with seasonal trends in
microbial contamination.
Future studies investigating the relationship between fomites and respiratory illness could incorporate indicator bacteria with specificity to nasal secretions or saliva
by identifying organisms from, for example, recent microbiota studies (Frank et al.,
2010). The bacteria used as indicators of microbial contamination in this study are
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
113
typically used as indicators of fecal contamination, and are therefore more typically
associated with gastrointestinal illness than with respiratory illness. However, the
significant findings of our results suggest that presence of enterococci may be an
appropriate predictor of respiratory symptoms.
5.6
Acknowledgments
The authors acknowledge Lauren Sassoubre, Isaias Espinoza, and Elfego Felix for their
assistance on site and in the laboratory, as well as Todd Russell, Thienan Nguyen, and
Francisco Tamayo for their assistance in the laboratory. The Boehm Research Group
provided helpful suggestions for study design and data analysis. The authors also
acknowledge Gojo Industries, Inc, for providing Purell Alcohol Based Hand Sanitizer
to the participating child care centers. Gojo Industries was not otherwise involved in
the study. The research has been funded, in part, by the UPS Foundation Endowment
Fund at Stanford University and the United States Environmental Protection Agency
(EPA) under the Science to Achieve Results (STAR) Graduate Fellowship Program.
EPA has not officially endorsed this publication and the views expressed herein may
not reflect the views of the EPA.
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
5.7
Tables
114
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
Surface
Tested
ball
7
block
11
book
4
chair
10
computer
5
doorknob
17
faucet
21
floor
3
glue container
1
kitchen surfaces
3
marker
5
mirror
1
playground
29
sandbox
10
shelf
3
soap dispenser
1
storage bin
4
table
59
toilet
6
toothbrush
1
toy
92
tray
1
water table
5
Total
299
Enterococci
1 (5%)
1 (33%)
1 (20%)
4 (14%)
2 (20%)
1 (2%)
9 (10%)
19 (6%)
115
Fecal Coliform
2 (10%)
1 (10%)
1 (2%)
5 (5%)
9 (3%)
Table 5.1: Summary of environmental fomites sampled along with the number and
corresponding percent of samples with detectable (≥5 CFU per 100cm2 ) enterococci
and fecal coliform. No E. coli were detected on fomites
371
344
Entero-
Noro- gI
Noro- gII
TA
Primers
Reference
◦
( C)
55
F: 5’- ACCGGATGGCCAATCCAA -3’
Jaykus et al., 1996
R: 5’- CCTCCGGCCCCTGAATG -3’
50
F: 5’- CTGCCCGAATTYGTAAATGA -3’
Kojima et al., 2002
R: 5’- CCAACCCARCCATTRTACA -3’
Lyman et al., 2009
50
F: 5’- CNTGGGAGGGCGATCGCAA -3’
Kojima et al., 2002
R: 5’- CCRCCNGCATRHCCRTTRTACAT -3’ Lyman et al., 2009
Table 5.2: Amplicon size, annealing temperature, and primers for PCR detection of enterovirus (Entero-) and
norovirus (Noro-) gI and gII
Size
(bp)
192
Target
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
116
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
Characteristics
Number of parents or guardians interviewed
Site A
Site B p − value
28
34
30
22-44
29
22-48
0.91
86
7
7
97
0
3
0.28
43
21
36
44
15
41
0.77
5.2
2.2
4.9
1.6
0.45
0.02
21
38
0.24
18
7
25
32
15
9
24
15
0.97
Response rate (interviewed / enrolled)
Age of parent or guardian respondent (years)
Mean
Range
Self-reported ethnicity (%)
Hispanic
African American
Other
Type of family residence %)
Single Family
Duplex
Apt. Complex
Mean no. residents in child’s household
Total
Under 6 yo
Children from families with pets (%)
Children with chronic disease (%)
Asthma
Other
Total
Children on any medications (%)
0.84
0.2
Table 5.3: Child Care Center Population Demographics
117
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
Characteristic
Baseline
No. gastrointestinal illness in past 6 mo. (%)
0
1
2 or more
No. respiratory illness in past 6 mo. (%)
0
1
2 or more
Site A
Site B
89
4
7
56
26
9
32
32
36
3
21
74
8.24
8.04
7.80
6.80
7.40
6.60
4
71
21
18
6
56
24
3
7.86
7.03
9.40
11.20
11.10
7.30
4
48
32
16
0
26
59
15
16
44
0
44
Healthy child index (mean)
Handwashing rate per day (mean)
Parent
Child
Likelihood of child illness in next month (%)
High
Some
None
Don’t Know
Follow-up
Healthy child index (mean)
Handwashing rate per day (mean)
Parent
Child
Likelihood of child illness in next month (%)
High
Some
None
Don’t Know
Child was ill during study (%)
Gastrointestinal
Respiratory
Table 5.4: Child Care Center Population Health and Hygiene Knowledge
118
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
Site A
Category
Total No. Person-Days
Absences
Total
Illness-Related
Illness
Total Illness∗
Symptomatic Illness
Respiratory
Total
Gastrointestinal
Total
New Episodes
Total
Respiratory
Gastrointestinal
Fever Only
Unspecified
119
Site B
Children
Staff
Children
Staff
Total
n = 37
2560
n=3
229
n = 33
2618
n = 3-5
212
5619
281
135
7
2
339
196
9
2
636
335
368
306
57
57
714
669
31
31
1170
1063
288
57
635
30
1010
14
1
22
1
38
106
67
5
5
31
7
7
0
0
0
113
82
9
9
13
6
5
0
0
0
232
161
14
14
44
Table 5.5: Number of person-days with recorded attendance and symptomatic illness
subset by child care facility and site. ∗ Total Illness is the combination of recorded
symptomatic illness and illness-related absenteeism with unspecified symptoms.
Obs.
407
373
294
205
188
274
443
534
446
338
223
184
223
359
420
Ill.
89
80
61
41
34
53
89
101
72
53
33
38
59
77
82
Coef.
-1.95
-1.88
-2.24
-1.91
-2.65
-2.60
-2.52
-2.10
-2.11
-2.27
-2.29
-2.63
-2.22
-2.01
-1.99
Intercept
SE Pr(>|W|)
0.29 <0.001
0.32 <0.001
0.39 <0.001
0.30 <0.001
0.44 <0.001
0.39 <0.001
0.33 <0.001
0.30 <0.001
0.33 <0.001
0.34 <0.001
0.37 <0.001
0.38 <0.001
0.42 <0.001
0.33 <0.001
0.30 <0.001
Coef.
0.41
0.34
0.25
0.14
0.34
0.34
0.41
0.40
0.31
0.15
0.38
0.99
0.32
0.19
0.00
Hands
SE Pr(>|W|)
0.17 0.015
0.18
0.053
0.20
0.213
0.21
0.492
0.36
0.343
0.22
0.112
0.15 0.005
0.14 0.003
0.15 0.041
0.18
0.393
0.24
0.107
0.22 <0.001
0.20
0.116
0.16
0.250
0.16
0.987
Coef.
0.33
-0.12
0.05
0.37
0.91
0.69
0.34
0.27
0.28
0.23
0.15
1.37
0.79
0.25
0.01
Fomites
SE Pr(>|W|)
0.21
0.106
0.22
0.580
0.25
0.843
0.31
0.227
0.42 0.030
0.30 0.023
0.19
0.069
0.17
0.122
0.17
0.104
0.22
0.300
0.33
0.649
0.42 0.001
0.32 0.014
0.22
0.257
0.23
0.956
Coef.
0.42
0.75
1.28
0.47
1.25
1.22
1.16
0.53
0.21
0.73
0.39
0.55
1.31
0.81
1.00
Site
Correlation
SE Pr(>|W|) Estimate SE
0.33
0.199
0.15
0.05
0.34 0.027
0.16
0.06
0.41 0.002
0.32
0.16
0.40
0.237
0.07
0.07
0.42 0.003
-0.01 0.07
0.40 0.002
0.04
0.05
0.38 0.002
0.18
0.07
0.33
0.102
0.16
0.05
0.38
0.587
0.19
0.07
0.39
0.058
0.11
0.05
0.47
0.414
0.08
0.04
0.46
0.235
0.10
0.10
0.43 0.002
0.12
0.07
0.37 0.029
0.16
0.06
0.34
0.003
0.11
0.04
Table 5.6: Parameters of generalized estimating equation for respiratory illness as function of enterococci on
hands and fomites. “Lag” is the number of days between collection of health data and the collection of data for
contamination on surfaces where a positive lag implies that health outcomes preceded microbial contamination.
“Obs.” is the number of observations with both health measurements and microbial contamination data. “Ill.” is
the number of observations when respiratory illness was observed,“Hands” is the number of enterococci detected
on hands, “Fomites” is the fraction of fomites sampled with detectable enterococci, “Site” is the facility, with the
coefficients representing the difference ifor Site B relative to Site A, “Coef.” and “SE” are the coefficient and
standard error of the correlation coefficient. “Pr(>|W|)” is the significance, with values less than 0.05 considered
significant and highlighted in bold.
Lag
7
6
5
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
120
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
5.8
Figures
121
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
Proportion Absent Per Day
1.0
(a) Absences
0.8
Children - Site A (n = 37)
Children - Site B (n = 33)
Staff - Site A (n = 3)
Staff - Site B (n = 3-5)
0.6
0.4
0.2
n
Ju
1-
ay
ar
pr
-A
M
8-
14
-M
21
b
-Fe
1.0
25
b
Fe
2-
0.0
Proportion Absent due to Illness Per Day
122
(b) Illness-Related Absences
0.8
Children - Site A (n = 37)
Children - Site B (n = 33)
Staff - Site A (n = 3)
Staff - Site B (n = 3-5)
0.6
0.4
0.2
n
ay
Ju
1-
M
8-
ar
pr
-A
14
-M
21
b
-Fe
25
b
Fe
2-
0.0
Figure 5.1: Time series of the proportion of children and staff who (a) are absent, and
(b) are absent due to illness. The shaded portion of the figures represents five days
(April 13–April 17) when no child care classes were held and no data were collected
Proportion with Respiratory Symptoms Per Day
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
1.0
(a) Respiratory Symptoms
0.8
Children - Site A (n = 37)
Children - Site B (n = 33)
Staff - Site A (n = 3)
Staff - Site B (n = 3-5)
0.6
0.4
0.2
(b) Gastrointestinal Symptoms
0.8
Children - Site A (n = 37)
Children - Site B (n = 33)
Staff - Site A (n = 3)
Staff - Site B (n = 3-5)
0.6
0.4
0.2
ay
n
Ju
1-
M
8-
ar
pr
-A
14
-M
b
b
Fe
-Fe
21
25
0.0
2-
Proportion with Gastrointestinal Symptoms Per Day
n
ay
Ju
1-
M
8-
ar
b
pr
-A
14
-M
21
-Fe
25
b
Fe
2-
0.0
12
No. New Illness Episodes Per Day
123
(c) New Illness Episodes
10
Children - Site A (n = 37)
Children - Site B (n = 33)
Staff - Site A (n = 3)
Staff - Site B (n = 3-5)
8
6
4
2
n
ay
Ju
1-
M
8-
ar
pr
-A
14
-M
21
b
-Fe
25
b
Fe
2-
0
Figure 5.2: Time series of the proportion of children and staff who (a) have respiratory
symptoms, and (b) have gastrointestinal symptoms. Also presented is a time series of
the first day of (c) new illness episodes. The shaded portion of the figures represents
five days (April 13–April 17) when no child care classes were held and no data were
collected
Proportion of Hands with Detectable Bacteria
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
1.0
(a) Hand Contamination at Site A
0.8
enterococci - AM
enterococci - PM
fecal coliform - AM
fecal coliform - PM
enterovirus neg / pos
0.6
0.4
0.2
Under Limit of Detection
0.0
n
ay
Ju
1-
M
8-
ar
b
pr
-A
-M
14
21
-Fe
1.0
25
b
Fe
2-
Proportion of Hands with Detectable Bacteria
124
(b) Hand Contamination at Site B
0.8
enterococci - AM
enterococci - PM
fecal coliform - AM
fecal coliform - PM
enterovirus neg / pos
0.6
0.4
0.2
Under Limit of Detection
0.0
n
ay
Ju
1-
M
8-
ar
b
pr
-A
-M
14
21
-Fe
25
b
Fe
2-
Figure 5.3: Time series of the proportion of hand samples with detectable bacteria
at (a) Site A and (b) Site B. The shaded portion of the graph represents five days
(April 13–April 17) when no child care classes were held and no data were collected.
Sampling visits when no samples had bacterial densities above the lower limit of
detection on hands (≥ 5 CFU per two hands) are marked by columns with heights
equal to the line marked “Limit of Detection”. Samples tested for enterovirus are
under the abscissa corresponding to the sample’s date. Each ◦ represents a negative
sample and • represents a positive sample
Proportion of Surfaces with Detectable Bacteria
CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS
1.0
(a) Environmental Contamination at Site A
0.8
enterococci - AM
enterococci - PM
fecal coliform - AM
fecal coliform - PM
0.6
0.4
0.2
Under Limit of Detection
ay
n
Ju
1-
M
8-
ar
pr
-A
14
M
21
b
b
Fe
1.0
-Fe
25
0.0
2-
(b) Environmental Contamination at Site B
0.8
enterococci - AM
enterococci - PM
fecal coliform - AM
fecal coliform - PM
0.6
0.4
0.2
Under Limit of Detection
n
ay
Ju
1-
M
8-
ar
pr
-A
14
-M
21
b
b
Fe
2-
0.0
-Fe
25
Proportion of Surfaces with Detectable Bacteria
125
Figure 5.4: Time series of the proportion of fomites sampled with detectable bacteria
on hands at (a) Site A and (b) Site B, and on fomites at (a) Site A and (b) Site
B. The shaded portion of the graph represents five days (April 13–April 17) when
no child care classes were held and no data were collected. Sampling visits when
no samples had bacterial densities above the lower limit of detection on fomites (≥
2.5 CFU per 25 cm2 ) are marked by columns with heights equal to the line marked
“Limit of Detection”.
Chapter 6
Conclusions and Future Directions
6.1
Conclusions
Conclusion 1: Virus transfers readily between surfaces
Chapter 2 investigates the fraction of virus that transfers between a fingerpad
and a glass surface. From the study, we demonstrate that the mean, median, and
standard deviation of the fraction of virus transferred between a fingerpad and glass
surface is 0.23, 0.18, and 0.22, respectively. These findings are of similar order of
magnitude as findings in previous literature on virus transfer between nonporous
surfaces and fingerpads (Ansari et al., 1991; Mbithi et al., 1992; Rusin et al., 2002).
The findings demonstrate that the amount of virus transferred, on a single contact,
to the fingerpad from a contaminated fomite is at a similar order of magnitude as
the original level of contamination. Chapter 2 also demonstrated that specific factors
investigated (e.g., hand washing, direction of transfer, and virus species) significantly
influenced the fraction transferred. However, the small effect size (5-10% of total
fraction transferred) of the factors on viral transfer suggests the factors likely have
little impact on infection risk from fomites.
Conclusion 2: Density of microorganisms on surfaces is both increased
by, and leads to, adverse health outcomes
126
CHAPTER 6. CONCLUSIONS
127
A major finding of this dissertation is evidence of a causal link between density of
microorganisms on surfaces and risk of infection. Chapter 3 demonstrates that in a
model of child-fomite interaction, the concentration of virus on the child’s previously
uncontaminated hands will equal the concentration on the fomite within minutes due
to frequent, repetitive hand-surface contacts (Figure 3.3). At that time, hand-mouth
contacts will contribute to ingested dose, and therefore risk of illness. This will occur
even if the fomite is removed. In the model, the initial concentration on the hands
is directly linked to the likelihood of infection. As Table 3.2 demonstrates, a tenfold
increase in the concentration of virus on the model fomite increased the child’s dose
one hundred fold, even after only 10 minutes of child-fomite interaction. Therefore,
the model suggests that high concentrations of virus on surfaces are indicative of
increased risk of illness.
If microbial contamination on surfaces are indicative of increased risk of illness,
it is likely that a field scale study investigating temporal trends in contamination
and health outcomes would demonstrate associations. Multiple previous studies have
suggested that a significant correlation exists between microbial contamination and
health (Van et al., 1991; Butz et al., 1993; Laborde et al., 1994). However, these
studies have shown correlations based on sampling fomites only once, or based on
multiple samplings that all occur after outbreaks. Without temporal sampling during
both outbreak and non-outbreak periods, causation between microbial contamination
and illness can not be inferred. Rather, increases in microbial contamination need
to precede increases in disease burden to demonstrate causation, as suggested in
Chapter 3. As there is a lag between dose and response for gastrointestinal and
respiratory viral disease of between 12 and 120 hours (See Table 1.1), increases in
illness due to microbial contamination need to be tracked with daily resolution. This
was the motivation for Chapter 5, the field-based study investigating temporal trends
in contamination and health.
Chapter 5, demonstrates that increases in microbial contamination lead to increases in adverse health outcomes. Using enterococci as an indicator of bacterial
contamination, respiratory disease is significantly and positively associated with contamination four and five days prior to illness. The lag of four or five days is consistent
CHAPTER 6. CONCLUSIONS
128
with incubation periods (typically between 1-4 days) for respiratory illness. Not only
does the work in Chapter 5 suggest that microbial contamination contributes to respiratory disease burden, but it also demonstrates that microbial contamination is
caused by respiratory disease burden. Specifically, enterococci on the hands is significantly associated with symptomatic illness on the same day as symptoms, as well
as on both the day before and the day after. This finding holds not only for the
symptoms reported by the child care center staff, but also for the symptom of visible
runny nose as reported by the research team during hand sample collection.
Conclusion 3: Virus sampling methods on fomites should be standardized
Evidence that microbial contamination contributes to respiratory illness supports
the need for standardized fomite sampling (Chapter 4). In the study in child care
centers (Chapter 5), enterococci was used as an indicator of microbial contamination.
The next research step is to sample hands and fomites for etiological agents in addition
to indicators. Significance of an association between etiological agents on surfaces and
increased illness (and accounting for the three to five day incubation period) would
provide stronger evidence of the role of fomites in respiratory disease transmission
in child care centers. Indicators such as enterococci are more easier to detect in
the environment than pathogens. Therefore, etiological agents may be detectable on
fewer than the 6% of samples with detectable enterococci identified in Chapter 5.
To improve detection of virus on surfaces, Chapter 4 suggests an effective sampling
method. The use of polyester-tipped swabs in conjunction with 1/4 strength Ringer’s
(hereafter referred to as “Ringer’s”) or saline solution resulted in significantly increased detection of infective virus relative to other methods tested. Using MS2
bacteriophage as a model virus, polyester-tipped swab with Ringer’s recovered 60%
more than the mean total virus recovered using other methods tested in the study.
The literature review included in the chapter also demonstrated that polyester-tipped
swabs were significantly associated with higher fraction of samples with detectable
virus.
Use of a standardized method will also allow cross comparisons of fomite-sampling
studies. In the 45 studies identified in Chapter 4 that sampled surfaces for pathogenic
CHAPTER 6. CONCLUSIONS
129
virus, the authors used 12 different implements and 4 different eluents. As implement
choice significantly influences recovery of virus from surfaces, comparison of outcomes
across studies is difficult unless the same sampling method is used. Standardizing the
sampling method, specifically through use of the polyester-tipped swabs in Ringer’s
or saline solution, would reduce this bias. Alternatively, quantifying the lower limit
of detection for the assay, or quantifying the virus detected on surfaces, would allow
cross comparison of studies.
6.2
Future Directions
My dissertation explored the role of fomites in disease transmission. I anticipate
my future research will build upon this knowledge, expand to incorporate additional
transmission routes, and continue investigating additional environmental reservoirs of
infectious disease. The goal of my future research will be to contribute to a holistic
understanding of human-environment interactions in infectious disease transmission.
In this section I describe several areas of future research following on my dissertation
work that will lead to new discoveries concerning the role of fomites in communicable
infectious disease.
Linking physicochemical properties of etiological agents to survival and
transmission
The movement and fate of virus through the environment via fomites may be influenced by virus physicochemical properties. In support, Chapter 2 demonstrates that
virus species significantly influences virus transfer to and from fomites. Similarly, the
work by Abad et al. (1994) demonstrates that virus species also influence virus persistence on fomites. The physicochemical properties of virus, therefore, are influential
in both virus transfer between surfaces and virus persistence. This is consistent with
work demonstrating the importance of physicochemical properties of virus movement
and fate in the subsurface Dowd et al. (1998).
CHAPTER 6. CONCLUSIONS
130
In Chapter 2, the characteristics that differed between the three bacteriophage
tested are the isoelectric point and hydrophobicity. Both characteristics have been
demonstrated to influence transport through the subsurface (Shields and Farrah,
2002). Unfortunately, as transfer of only three viruses was investigated, no trends
between fraction transferred and either isoelectric point or hydrophobicity were elucidated. In the estimate of the inactivation rate for MS2 bacteriophage, discussed in
Chapter 3, only one virus was studied and therefore no conclusions relating physicochemical properties to persistence can be drawn. Therefore, questions remain concerning the cause of the difference in transfer between the viruses and whether or
not the difference would be applicable to transfer of animal virus. Similar questions
concerning whether or not physicochemical properties of virus influence viral persistence on surfaces, and whether or not the differences in either persistence or transfer
contribute to increased efficacy in fomite-mediated transmission.
Incorporating secondary transmission into quantitative microbial risk
assessments of fomite-mediated transmission
Chapter 3 models the risk of infection for a single individual interacting with a
contaminated fomite. The model is among the first to incorporate complex humanfomite interactions, including sporadic, sequential contact events, into a quantitative
microbial risk assessment. However, the model only focuses on half of the fomitemediated transmission route (steps 2-4 of Figure 1.2). The model ignores the steps
leading to contamination of the fomite. Therefore, questions remain concerning the
influence of shedding of virus to fomites on resulting infectious disease transmission,
and the role of fomites in secondary infections (person-to-person spread).
Agent-based modeling provides a framework for modeling secondary transmission
rates due to indirect contact. Recently, infectious disease modeling has incorporated
environmental reservoirs into compartmental modeling (Li et al., 2009; Stilianakis
and Drossinos, 2010). In compartmental modeling, rate parameters drive movement
of individuals between compartments. In this manner, compartmental modeling assumes homogeneity and perfect mixing within compartments, as well rates of transfer
CHAPTER 6. CONCLUSIONS
131
between compartments (Rahmandad and Sterman, 2008). Fomite-mediated transmission, however, is an inherently spatial phenomenon: infection from a fomite can not
occur unless both an infected individual and a susceptible individual contact the same
fomite in that order. Therefore, an alternative method, such as agent-based modeling,
might prove more useful in understanding fomite-mediated transmission. Agent based
modeling allows for heterogeneity across individuals, as well as among the network
of their interactions (Rahmandad and Sterman, 2008). Defining both individuals as
agents capable of moving within predefined ranges, and fomites as stationary agents,
a framework for agent-based modeling of infectious disease transmission is suggested.
The work of Chapter 3 lays the groundwork for agent-based modeling of fomitemediated transmission. The child modeled in Chapter 3 is provided a defined set of
parameters (e.g., frequency and sequence of fomite contacts, likelihood of infection
given a dose). Similarly, the fomite parameters are predefined (e.g., inactivation rate
of virus, fraction virus transferred on contact). Replicating those agents, and defining
additional parameters (e.g., frequency of contacts between agents, shedding of agents
to others), would be among the first steps toward development of an agent-based
model. Using a representative closed system, such as a nursing home, office, or child
care center, could then provide an opportunity to validate the results by comparing
predicted outbreak patterns to documented patterns, as seen in (Bartlett III et al.,
1988; Iizuka, 2006). Once fomite-mediated transmission is modeled, characteristics of
fomite-mediated transmission could be explored. Additionally, interventions could be
implemented in the model to characterize likelihood of success in laboratory or field
settings.
Relative contribution of transmission routes to total respiratory and
gastrointestinal disease burden
Chapter 5 identified a significant association between respiratory illness and microbial contamination on surfaces. The daily resolution of health data provided an
opportunity to investigate causal links between illness and fomites. However, no
data were collected on direct contact, common vehicle, or airborne transmission.
CHAPTER 6. CONCLUSIONS
132
Although there is a significant association between health outcomes and microbial
contamination, the proportion of total respiratory illness attributable to indirect contact transmission is unknown. Therefore, the question of the relative contribution of
transmission routes, and fomite-mediated transmission in particular, remains. Efforts
to develop a systematic, evidence-based approach, to understanding relative contributions of transmission routes will contribute to development of interventions to reduce
overall disease burden.
Based on the sampling method of the work in Chapter 5, a multi-route exposure study could be performed. Airborne and common vehicle transmission could be
monitored by incorporating personal air monitoring and replicate food/water diets.
Simultaneously, data on subjects’ behaviors (e.g., contacts with other subjects, movement within the facility, contact with surfaces) could be collected via third-person or
videographic observations (Ferguson et al., 2006). An individual’s likelihood of illness could then be modeled as a function of both their behaviors and the presence
of etiological agents in the environment. The modeling could provide insight into the
relative contributions of transmission routes to disease burden.
Estimating the contribution of heterogeneous fomite use to variability in
infection risk
Current sampling protocol for estimating virus contamination on fomites relies on
subjective sampling choice. In Chapter 4, we identified over 40 unique publications
investigating virus contamination on fomites. In the publications, as in the fomites
sampled in Chapter 5, the fomite choice for sampling was subjectively chosen by the
research staff. No publication included a sampling protocol that identified the fomites
that should be sampled prior to the study.
However a fomite’s contribution to disease transmission is likely a function of its
use as well as the presence of microbial contamination. For example, in Chapter 5
we modeled the interaction of a child with a contaminated toy ball. If the same
child was in a room of multiple toys, and only one or a couple were contaminated, the
child’s choice would increase the variability in the likelihood of infection. By choosing
CHAPTER 6. CONCLUSIONS
133
to interact with an uncontaminated toy, the child would reduce his risk of infection
to zero. Additional evidence is provided by Jiang et al. (1998), who suggested that
surfaces likely to be contacted by children were more likely contaminated with a DNA
marker seeded into a child care center.
Understanding the heterogeneity of fomite use would improve understanding of
fomite-mediated tranmsission. Fomite use could be identified through, for example,
sensors or videographic techniques. The data gleaned could then be incorporated in
sample choice for future fomite contamination studies, as well as in the interpretation
of results. Additionally, identification of highly used fomites could be used to tailor
environmental hygiene interventions.
Extension of models to nosocomial bacterial infections
Fomite-mediated transmission is an important route of nosocomial bacterial infections, and future research should extend the presented work to investigate transmission of bacterial infections in hospitals. The focus of the dissertation is on indoor transmission of viral respiratory and gastrointestinal disease. Motivation for
the focus is provided in Chapter 1, and includes the notion that viral infections,
unlike bacterial infections, can not readily be treated with antibiotics. Antibacterialresistant bacteria, however, are more frequently responsible for nosocomial infections.
The most common examples, methicillin-resistant Staphylococcus aureus (MRSA) and
vancomycin-resistant enterococci (VRE), are believed to be readily transmitted via
fomites. Evidence of detection and persistence of MRSA and VRE on surfaces supports the likelihood of fomite-mediated transmission .
There are both clinical and economic incentives for curbing nosocomial bacterial
infections. Approximately 1.7 million hospital acquired infections occurred in the U.S.
hospitals alone in 2002 (Klevens et al., 2007). This comes at an estimated cost of $700$2000 per case (Graves et al., 2008). Reductions in total healthcare expenditures,
therefore, may be achieved through increased infection control targeted to effectively
interrupting transmission routes (Graves et al., 2008). Future work extending the
dissertation to understanding transmission of hospital-acquired infections may aid in
the design and implementation of interventions for infection control.
Appendix A
Supplemental Material for Chapter
3: Equations Used in
Discrete-Time Model
Equations used to represent fomes-mouth contacts:
Change in concentration on both hands:
CH(tc ) = CH(tc−1 ) e(−kh ∆t)
(A.1)
Change in concentration on fomes:
CF (tc ) = (1 − T EF M SF )CF (tc−1 ) e(−kf ∆t)
(A.2)
Increase in dose:
DOSE = T EF M SF AF CF (tc−1 ) e(−kf ∆t)
134
(A.3)
APPENDIX A. SUPPLEMENTAL MATERIAL FOR CHAPTER 3
135
Equations used to represent hand-fomes contacts:
Change in concentration on hand in contact with fomes:
CH(tc ) = CH(tc−1 ) e(−kh ∆t) − T EF H SH (CH(tc−1 ) e(−kh ∆t) − CF (tc−1 ) e(−kf ∆t) )
(A.4)
Change in concentration on hand not in contact with fomes:
CH(tc ) = CH(tc−1 ) e(−kh ∆t)
(A.5)
Change in concentration on fomes:
CF (tc ) = CF (tc−1 ) e(−kf ∆t) − T EF H SH
AH
(CF (tc−1 ) e(−kf ∆t) − CH(tc−1 ) e(−kh ∆t) )
AF
(A.6)
Increase in dose:
Dose = 0
(A.7)
Equations used to represent hand-mouth contacts:
Change in concentration on hand in contact with mouth:
CH(tc ) = (1 − T EHM SM )CH(tc−1 ) e(−kh ∆t)
(A.8)
Change in concentration on hand not in contact with mouth:
CH(tc ) = CH(tc−1 ) e(−kh ∆t)
(A.9)
Change in concentration on fomes:
CF (tc ) = CF (tc−1 ) e(−kf ∆t)
(A.10)
DOSE = T EHM SM AH CH(tc−1 ) e(−kh ∆t)
(A.11)
Increase in dose:
APPENDIX A. SUPPLEMENTAL MATERIAL FOR CHAPTER 3
Variables
tc
tc−1
∆t = tc - tc−1
CH
CF
kh
kf
T EF M
T EF H
T EHM
SF
SH
SM
AF
AH
DOSE
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
time of the current contact
time of the previous contact
time between successive contacts
concentration of virus on surface of hand, virus/cm2
concentration of virus on surface of fomes, virus/cm2
inactivation rate of virus on hand, s−1 , base e
inactivation rate of virus on fomes, s−1 , base e
fraction of virus transferred from fomes to mouth
fraction of virus transferred between fomes and hand
fraction of virus transferred from hand to mouth
fraction of surface area of fomes in contact with mouth
fraction of surface area of hand in contact with fomes
fraction of surface area of hand in contact with mouth
surface area of fomes, cm2
surface area of hand, cm2
number of viral particles ingested
136
Appendix B
Supplemental Material for Chapter
4: Virus Recovery from Fomites
B.1
Tables
137
Year
1995
1999
2007
1998
First Author
Akhter
Asano
Bausch
Bellamy
Yes
Yes
Yes
AdenoEnteroAdeno-
No
Yes
Ebola
Entero-
Yes
Ebola
Yes
Yes
Influenza
VZV
Yes
Ill
Rhino-
Virus
RTPCR
RTPCR
culture
nPCR
EIA
culture
culture
culture
EIA
Assay
Rayon
Polyester
Polyester
Cotton
Swab
Swab
Swab
Swab
Swab
Implem
Saline
PBS
PBS
RPMI1640
TPB + Anti
TPB + Anti
TPB + Anti
TPB + Anti
TPB + Anti
Eluent
toilet bowl
telephone,
tap handle,
above
same as
cryoprobes
forceps,
gloves,
filter
conditioner
button, air
push-
channel
television
table,
handle,
door
N/A
N/A
N/A
N/A
sign chart
toys, vital
televisions,
handles,
toilet
Surfaces
3
1
0
18
0
0
0
0
12
Pos.
No.
448
28
28
72
146
146
146
146
146
Total
Frac
0.01
0.04
<0.04
0.25
<0.01
<0.01
<0.01
<0.01
0.08
Pos
No
No
No
Yes
No
No
No
No
No
LOD
Home
Hospital
Hospital
Home
Hospital
Hospital
Hospital
Hospital
Hospital
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
138
Year
2005
2005
2009
2009
First Author
Boone
Booth
Boxman
Boxman
No
Influenza
Noro-
NoroYes
Yes
Yes
No
Influenza
SARS-CoV
Yes
Ill
Influenza
Virus
qRTPCR
qRTPCR
RTPCR
RTPCR
RTPCR
RTPCR
Assay
Antistatic
Antistatic
Polyester
Polyester
Polyester
Polyester
Implem
Ringer’s
Ringer’s
VTM
Saline
LB
LB
Eluent
door
button,
elevator
handrail,
telephone,
cash desk,
knife grips
toilet seats,
cntrl
remote
table,
controls
remote
switches,
toilets light
keyboards,
phones,
dooknobs,
handles,
above
same as
areas
changing
diaper
floors,
counters,
drains,
dishcloths,
handles,
seats, fauct
toys, toilet
Surfaces
48
3
3
54
25
58
Pos.
No.
119
6
85
92
109
109
Total
Frac
0.40
0.50
0.04
0.59
0.23
0.53
Pos
Yes
Yes
No
No
No
No
LOD
Ship
Ship
Hospital
Home
DCC
DCC
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
139
Year
2009
1993
2002
2000
First Author
Bright
Butz
Carducci
Cheesebrough
Yes
No
Noro-
Yes
Noro-
HCV
Yes
Yes
NoroRota-
Yes
Ill
Influenza
Virus
nRTPCR
nRTPCR
nRTPCR
RTPCR
RTPCR
RTPCR
Assay
Cotton
Cotton
Cotton
Cotton
Rayon
Rayon
Implem
VTM
VTM
BE
PBS
Amies
Amies
Eluent
above
same as
cushions
phones,
tables,
toilet,
carpet,
holder
test tube
test tube,
plastic toys
handle,
handle, sink
toilet
fountain,
telephone,
above
same as
dispensers
tops, towel
counter-
handles,
doorknobs,
computers,
desks,
Surfaces
0
61
2
14
9
13
Pos.
No.
144
144
42
91
55
54
Total
Frac
<0.01
0.42
0.05
0.15
0.16
0.24
Pos
No
No
Yes
No
No
No
LOD
Hotel
Hotel
Hospital
DCC
Class
Class
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
140
Year
2004
2008
2004
1989
2008
2003
2005
First Author
Chen
Diggs
Dowell
Ferenczy
Fischer
Froio
Gallimore
Yes
Yes
HCV
Astro-
Yes
Yes
HBV
HCV
Yes
Yes
SARS-CoV
HPV
Yes
Yes
Yes
Ill
SARS-CoV
Noro-
SARS-CoV
Virus
hnRTPCR
RTPCR
antigen
RTPCR
DBH
culture
RTPCR
RTPCR
RTPCR
Assay
Cotton
Cotton
Cotton
Cotton
Polyester
Polyester
Polyester
Swab
Cotton
Implem
PBS
Saline
Saline
BE
Saline
VTM
VTM
N/R
VTM
Eluent
equip.
medical
toilet taps,
console,
cuff
pressure
blood
machine
dialysis
(glass)
crack pipes
cryoprobes
forceps,
gloves,
N/A
handrail
mouse,
computer
N/R
(plug)
outlet
bedding,
table,
bookshelf,
chair,
bedside
fountain,
water
drinking
Surfaces
4
1
1
1
36
0
22
1
9
Pos.
No.
12
64
64
51
100
90
90
25
100
Total
Frac
0.33
0.02
0.02
0.02
0.36
<0.01
0.24
0.04
0.09
Pos
No
No
Yes
Yes
Yes
No
No
No
Yes
LOD
Hospital
Hospital
Hospital
Outside
Hospital
Hospital
Hospital
Class
Hospital
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
141
Year
2006
2008
2008
2006
First Author
Gallimore
Gallimore
Girou
Goldhammer
Rhino-
HCV
No
No
No
No
Rota-
Rota-
Yes
Rota-
No
No
Noro-
Noro-
Yes
Noro-
No
No
Astro-
Astro-
Yes
Ill
Astro-
Virus
culture
nRTPCR
nRTPCR
hnRTPCR
hnRTPCR
nRTPCR
nRTPCR
nRTPCR
nRTPCR
hnRTPCR
hnRTPCR
Assay
Cotton
N/R
Cotton
Cotton
Cotton
Cotton
Cotton
Cotton
Cotton
Cotton
Cotton
Implem
VTM
N/R
Saline
Saline
Saline
Saline
Saline
Saline
Saline
Saline
Saline
Eluent
equipment
exercise
table
machine,
dialysis
telephone
switch,
light
toilet tap,
above
same as
light switch
toilet tap,
above
same as
above
same as
above
same as
above
same as
above
same as
equip.
medical
phone,
toilet taps,
console,
game
Surfaces
63
6
28
12
3
17
7
7
21
2
4
Pos.
No.
100
82
242
242
242
121
33
99
55
66
88
Total
Frac
0.63
0.07
0.12
0.05
0.01
0.14
0.21
0.07
0.38
0.03
0.05
Pos
No
Yes
No
No
No
No
No
No
No
No
No
LOD
Gym
Hospital
Hospital
Hospital
Hospital
Hospital
Hospital
Hospital
Hospital
Hospital
Hospital
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
142
Year
1998
2007
1982
2008
2007
2009
1983
First Author
Green
Gurley
Gwaltney
Hamada
Jones
Kawahara
Keswick
Rota-
MCV
Noro-
Adeno-
Rhino-
Nipah
Noro-
Virus
No
Yes
Yes
Yes
Yes
No
Yes
Ill
antigen
nPCR
RTPCR
RTPCR
culture
RTPCR
RTPCR
Assay
Cotton
Cotton
Rayon
N/R
Cotton
Cotton
Cotton
Implem
MEM + Anti
PBS
Amies Gel
N/R
BHIB
MEM + Anti
VTM
Eluent
sink, hands
door knob,
diaper pail,
keyboard
chair,
drawers,
toy
pianaca,
locker,
desk,
doorknobs
surfaces,
kitchen
surfaces,
bathroom
lamp
equiprment,
medical
frame, lens,
glasses
tiles
frame
wall, bed
commodes
curtains,
lockers,
Surfaces
4
8
11
17
20
11
11
Pos.
No.
25
9
14
21
47
468
36
Total
Frac
0.16
0.89
0.79
0.81
0.43
0.02
0.31
Pos
No
No
No
No
No
No
No
LOD
DCC
Home
Boat
Hospital
Home
Hospital
Ward
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
143
Year
2002
2009
2009
2008
2009
2009
First Author
Kuusi
Lederman
Lessa
Lopez
Lyman
Pappas
No
No
Influenza
Yes
RotaRSV
Yes
NoroNo
Yes
Astro-
Picorna-
Yes
Yes
Yes
Yes
Yes
Ill
Adeno-
VZV
Adeno-
Orthopox-
Noro-
Virus
RTPCR
RTPCR
RTPCR
RTPCR
RTPCR
RTPCR
PCR
qPCR
qPCR
qPCR
RTPCR
Assay
Cotton
Cotton
Cotton
N/R
N/R
N/R
N/R
Polyester
Swab
Polyester
Cotton
Implem
BHIB + BSA
BHIB + BSA
BHIB + BSA
N/R
N/R
N/R
N/R
PBS
Saline
Saline
PBS
Eluent
toys
toys
toys
N/R
N/R
N/R
N/R
fixtures
carts, and
frames,
chairs, bed
dust from
keyboard
cabinet,
table,
bedside
bedrain,
seat, cup
seat, car
booster
nighstand,
toys,
slipper,
washcloth,
ointment,
toilet seat
handle,
door
bathroom
handle,
ultrasound
Surfaces
0
0
10
38
11
9
16
18
7
8
4
Pos.
No.
18
17
52
38
40
45
27
26
37
25
30
Total
Frac
<0.06
<0.06
0.19
>0.97
0.28
0.20
0.59
0.69
0.19
0.32
0.13
Pos
Yes
Yes
Yes
No
No
No
No
Yes
No
No
No
LOD
Hospital
Hospital
Hospital
DCC
DCC
DCC
DCC
Hospital
Hospital
Home
Hotel
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
144
Year
1987
2007
2006
1999
First Author
Piazza
Runner
Russell
Soule
Yes
Yes
HAV
HCV
Rota-
Yes
No
Yes
HBV
Adeno-
Yes
HIV
No
No
Influenza
HBV
Ill
Virus
RTPCR
TIGER
PCR
PCR
PCR
PCR
antigen
RTPCR
Assay
Cotton
Polyester
Swab
Swab
Swab
Swab
Cotton
Cotton
Implem
MEM
VTM
Water
Water
Water
Water
+ BSA
Saline
BHIB + BSA
Eluent
washbasins
struments,
medical in-
tables,
cloths,
cleaning
playmats,
handles,
rifles
lockers,
pillows,
above
same as
above
same as
above
same as
container
sharps
disposable
arms
headrest,
dental chair
benches,
work
toys
Surfaces
22
163
4
2
2
3
12
1
Pos.
No.
45
629
30
30
30
30
190
18
Total
Frac
0.49
0.26
0.13
0.07
0.07
0.10
0.06
0.06
Pos
No
No
No
No
No
No
No
Yes
LOD
Hospital
Military
Hospital
Hospital
Hospital
Hospital
Dentist
Hospital
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
145
Year
2002
2002
1992
2007
2005
2001
First Author
Strauss
Widdowson
Wilde
Winther
Wu
Yoshikawa
VZV
Noro-
Yes
Yes
No
Yes
Rota-
Rhino-
No
Yes
No
Ill
Rota-
Rota-
HPV
Virus
nPCR
RTPCR
RTPCR
RTPCR
RTPCR
RTPCR
nPCR
Assay
Cotton
Cotton
Cotton
Cotton
Cotton
Cotton
Cotton
Implem
RPMI1640
Saline
BHIB + BSA
BHIB
BHIB
PBS
PBS
Eluent
door
chair, table,
back of
rail
seat, bed
table, toilet
button,
elevaot
phones, etc.
faucet,
pens,
handles,
Door
area
floor, child
toy balls,
child area
toy balls,
lightswitch
cupboard,
linen
door handle
bathroom
lamp,
nation
bed, exami-
amination
panel, ex-
bed control
Cyroguns,
Surfaces
11
5
52
15
2
2
37
Pos.
No.
27
10
150
57
65
94
102
Total
Frac
0.41
0.50
0.35
0.26
0.03
0.02
0.36
Pos
Yes
No
No
No
No
No
Yes
LOD
Home
LTC
Hotel
DCC
DCC
Hospital
Hospital
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
146
First Author
Year
Virus
Assay
Implem
Eluent
Surfaces
Pos.
No.
viations provided in Table B.2.
Table B.1: A summary of the articles included in the analysis using the abbre-
Ill
Total
Frac
Pos
LOD
Locale
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
147
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
Amies
Anti
BE
BHIB
BSA
Culture
DBH
DCC
EIA
Eluent
HAV
HBV
HCV
HIV
hnPCR
HPV
Ill
Implem
LB
LOD
LTC
MCV
MEM
nPCR / nRTPCR
N/R
PBS
PCR
qPCR / qRTPCR
Ringer’s
RSV
RT-PCR
SARS-CoV
Surfaces
Swab
Total
TPB
VTM
VZV
148
Amies medium
Antibiotics
Beef extract
Brain heart infusion broth
Bovine serum albumin
Cell culture
Dot blot hybridization
Day care center
Enzyme immunoassay
Eluent type used
Hepatitis A virus
Hepatitis B virus
Hepatitis C virus
Human immunodeficiency virus
Hemi-nested PCR
Human papillomavirus
Clinically infected individual was present
Implement type used
Letheen broth
Lower limit of detection is reported in article
Long Term Care
Molluscum contagiosum virus
Minimal essential medium
Nested PCR / RTPCR
Not reported by author
Phosphate buffered saline
Polymerase chain reaction
quantitative PCR / RTPCR
1/4 strength Ringer’s solution
Respiratory syncytial virus
Reverse transcription PCR
Severe acute respiratory syndrome - corona virus
Type of surfaces with detectable target
Unreported type of swab
Total number of surfaces sampled
Tryptose phosphate broth
Viral transport medium
Varicella zoster virus
Table B.2: The abbreviations, and corresponding definitions, used in Table B.1 and
Table B.3.
APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4
Virus
No. Studies
Adeno6
Astro5
Ebola
2
2
EnteroHAV
1
HBV
3
HCV
5
1
HIV
HPV
2
Influenza
7
MC
1
Nipah
1
14
NoroOrthopox1
Picorna1
Rhino3
Rota11
1
RSV
SARS-CoV
4
VZV
3
Total
74
Samples Collected
1006
453
56
594
30
284
269
30
202
546
9
468
1019
25
52
297
957
17
365
125
6804
No. Pos.
203
22
1
3
2
15
14
3
73
151
8
11
204
8
10
135
161
0
34
47
1105
149
Frac. Pos.
.202
.049
.018
.005
.067
.053
.052
.100
.361
.277
.889
.024
.200
.320
.192
.455
.168
.000
.093
.376
.162
Table B.3: A summary of the articles included in the analysis using the abbreviations
provided in Table B.2
Appendix C
Supplemental Material for Chapter
5: Fomites and Health in Child
Care Centers
C.1
Methods
C.1.1
Statistics
Most statistics were performed using PASW Statistics 18.0.2. (SPSS: An IBM Company, Chicago, IL, USA). A significance level of α <0.05 was used throughout the
study.
Survey.
The results of the surveys were compared across two sites using a Z-test for two proportions for percentage data (pet ownership, medication, and chronic illness), Pearson’s χ2 for categorical data (ethnicity, type of household), and Mann-Whitney U test
or Kruskal-Wallis one-way analysis of variance for two or more independent samples
of ordinal data( Changes in parent and child handwashing rates and healthy child index between the baseline and follow-up surveys were examined using a matched-pair
t-test.
150
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
151
Microbial Contamination.
Significance of self-reported health on microbial contamination was determined using
Kruskal-Wallis one-way analysis of variance. Mann-Whitney U tests were used to
assess significance of associations of hand contamination with visible signs (dirt on
hands, dirt under fingernails, and runny nose), time of class (morning or afternoon),
site (A or B), location of sampling (indoor or outdoor). Mann-Whitney U tests were
also used to assess significance of associations of environmental contamination with
time of class (morning or afternoon), site (A or B), and location of sampling (indoor
or outdoor).
Bivariate Correlations
Bivariate Spearman rank correlations were used to investigate correlations between
environmental contamination (weekly fraction of samples with /geq5 CFU enterococci or fecal coliform), hand contamination (daily and weekly fraction of samples
with /geq5.4 CFU enterococci or fecal coliform per two hands), and health (daily and
weekly respiratory illness, gastrointestinal illness, absences, illness-related absences,
and number of unique illness episodes). Results from the bivariate correlations were
then used to identify fecal indicator bacteria (e.g., enterococci, fecal coliform, or E.
coli ) and health outcome (e.g. respiratory illness, gastrointestinal illness, absences,
illness-related absences or unique illness episodes) to include in model of health outcome as a function of microbial contamination.
Health Outcome as a Function of Contamination
To investigate associations between surface contamination and health, health outcomes were modeled as functions of the density of enterococci on hands, the presence
/ absence of enterococci on at least one sampled environmental surface, and site.
Preliminary bivariate correlations suggest enterococci is a more appropriate indicator
of hand and surface contamination then fecal coliform and E. coli, so enterococci was
used as the dependent variable. Inter - individual correlation in the longitudinal data
was accounted for using generalized estimating equations (GEE) with a logit link
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
152
function clustered on individual St Sauver et al. (1998). We assumed data correlation was independent in time, and use a compound correlation structure. The GEE
analysis was performed using the “geeglm” function in the “geepack” package in R
(version 2.11.1, R Foundation for Statistical Computing, Vienna, Austria). To infer casual links between surface (hand and environmental) contamination and health
(where respiratory illness has an incubation time of 2-5 days Long et al. (1997)), we
used separate GEEs to model health outcomes as a function of microbial contamination at daily lags of up to plus and minus seven days. Only the subset of data with
measured health outcomes and corresponding microbial contamination data at the
specified lag was included (i.e., missing values were removed from analysis). Health
outcomes explored included illness-related absences, respiratory illness, and onset of
unique illness episodes. The low prevalence of gastrointestinal illness during the study
(see Results) precluded analysis of gastroenteritis.
C.2
Results
C.2.1
Bivariate Correlations
C.2.2
Hand Contamination and Health Data.
Correlations between hand contamination and health data were performed by aggregating each individual’s data over the duration of the study and investigating
associations between the fraction of total days individuals experienced an adverse
health outcome to the mean density of bacteria on their hands over the duration of
the study. The sample size was, therefore, seventy-seven, equal to the number of
individuals who were both enrolled in the study and assented to at least one hand
sample.
Enterococci on hands was significantly correlated to respiratory illness (ρs = 0.276,
p = 0.015), but not gastrointestinal illness(ρs = 0.034, p = 0.771), absences (ρs =
−0.047, p = 0.688), illness-related absences (ρs = 0.046, p = 0.693), or number of
unique illnesses (ρs = −0.093, p = 0.422). Neither the density of fecal coliform nor E.
coli on hands were significantly correlated to any health outcomes. Specifically, fecal
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
153
coliform was not significantly correlated to respiratory illness (ρs = 0.128, p = 0.267),
gastrointestinal illness(ρs = 0.148, p = 0.199), absences (ρs = −0.041, p = 0.726),
illness-related absences (ρs = −0.022, p = 0.850), or number of unique illnesses
(ρs = −0.031, p = 0.786). Similarly, E. coli was not significantly correlated to
respiratory illness (ρs = 0.122, p = 0.290), gastrointestinal illness(ρs = −0.056,
p = 0.626), absences (ρs = 0.034, p = 0.769), illness-related absences (ρs = 0.127,
p = 0.270), or number of unique illnesses (ρs = −0.140, p = 0.224).
C.2.3
Hand Contamination and Environmental Contamination.
The fraction of environmental fomites with detectable enterococci on a given day was
not correlated with corresponding fraction of hand samples with detectable enterococci (ρs = 0.206, p = 0.108). Similarly, there was no significant correlation for fecal
coliform (ρs = 0.069, p = 0.591).
C.2.4
Environmental Contamination and Health Data.
The fraction of samples with detectable enterococci at each facility is significantly
correlated with the fraction of individuals with respiratory illness symptoms during
the same week (ρs = 0.297, p = 0.018), but is not significantly correlated with
gastrointestinal illness (ρs = 0.157, p = 0.218), absences (ρs = −0.151, p = 0.237),
illness-related absences (ρs = 0.039, p = 0.761), or number of unique illness episodes
(ρs = 0.126, p = 0.326). There are no significant correlations for fecal coliform and
respiratory illness, (ρs = −0.023, p = 0.859), gastrointestinal illness, (ρs = 0.045,
p = 0.728), absences (ρs = 0.066, p = 0.606), illness-related absences (ρs = 0.088,
p = 0.490), or number of unique illness episodes (ρs = 0.170, p = 0.184).
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
C.2.5
154
Health Associations with Hand and Surface Contamination
Using generalized estimating equations to model illness-related absences as a function
of hand and environmental contamination, while controlling for site and incorporating
daily lags did not elucidate clear trends. Illness-related absences is significantly associated with hand and environmental contamination only sporadically (See Table C.2)
and is likely a result of false positive detection. Specifically, illness-related absences
is negatively associated with hand contamination as measured five days previously
(β = −0.958, p = 0.039) and positively associated with environmental surface contamination as measured three (β = 2.34, p < 0.001) and seven days later (β = 0.546,
p = 0.021).
New episodes of illness as a health outcome was also significantly associated with
hand and environmental contamination and hand hygiene only sporadically (See Table
C.1). False positive detection likely explains significant associations. Specifically, new
episodes of illness were positively associated with hand contamination as measured on
the same day (β = 0.613, p = 0.019) and with environmental surface contamination
as measured two days later (β = 1.49, p = 0.0136).
C.3
C.3.1
Discussion
Use of Multiple Comparisons
The use of fifteen GEEs to model health outcome at daily lags requires use of multiple
comparisons. The expected number of false positives is two, as estimated for fifteen
models with two variables (excluding intercept and adjustment for site) and a significance threshold of 0.05. In the model of respiratory illness, 9 of the 30 total variables
are significant. The likelihood of a false positivity rate of 8 or more variables, relying
on the assumption that all tests are independent, is less than 0.001% Storey (2003).
Therefore, most of the 9 significant variables are likely true positives. The clustering
of significant associations around specific lags (e.g. hands are significantly associated
with respiratory illness on -1,0, and +1 days, fomites on -4, and -5 days as well as +2
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
155
and +3 days) provides evidence that the findings are likely true. Random significant
associations, such as hands at a lag of +7 days are more likely false. Conversely, the
likelihood of 3 or more significant associations out of 30 significant tests, as observed
in the two models for new episodes of illness and illness-related absences is approximately 19%, suggesting that the majority of significant findings for the two models
are false positives.
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
C.4
Tables.
156
Obs.
439
395
328
214
204
284
455
572
472
365
233
193
231
370
440
Ill.
18
10
8
7
10
9
19
14
13
11
8
15
13
18
15
Coef.
-3.01
-2.89
-4.41
-2.79
-4.24
-4.75
-3.34
-4.03
-3.03
-2.84
-3.39
-3.14
-2.72
-2.71
-3.71
Intercept
SE Pr(>|W|)
0.32 <0.001
0.40 <0.001
0.68 <0.001
0.50 <0.001
0.66 <0.001
1.02 <0.001
0.45 <0.001
0.50 <0.001
0.45 <0.001
0.49 <0.001
0.51 <0.001
0.47 <0.001
0.42 <0.001
0.39 <0.001
0.55 <0.001
Coef.
-0.30
0.10
0.28
-0.32
0.53
-0.60
-0.33
0.62
0.11
-0.80
-0.08
0.48
-0.73
-0.71
0.01
Hands
SE Pr(>|W|)
0.33
0.361
0.32
0.747
0.41
0.484
0.38
0.402
0.47
0.255
0.67
0.372
0.40
0.409
0.25 0.014
0.40
0.784
0.61
0.187
0.42
0.850
0.31
0.118
0.39
0.060
0.44
0.104
0.41
0.990
Coef.
0.66
-1.61
0.23
0.62
1.11
0.58
0.02
0.54
-1.71
-0.11
-0.12
-0.16
0.19
0.47
-0.20
Fomites
Site
Correlation
SE Pr(>|W|) Coef. SE Pr(>|W|) Estimate SE
0.42
0.120
-0.58 0.56
0.307
0.00
0.04
1.12
0.151
-1.97 1.10
0.075
0.00
0.02
0.70
0.736
0.74 0.69
0.284
-0.02 0.05
0.86
0.468
-42.98 0.42 <0.001
-0.08 0.13
0.57 0.050
1.00 0.69
0.147
0.03
0.06
0.52
0.267
1.97 1.10
0.073
0.00
0.03
0.45
0.966
0.66 0.55
0.226
0.03
0.04
0.48
0.258
-0.99 0.63
0.117
0.00
0.01
0.94
0.071
-0.70 0.54
0.200
-0.02 0.04
0.55
0.845
-0.55 0.63
0.381
0.00
0.02
0.60
0.837
0.32 0.81
0.693
0.03
0.06
0.76
0.835
0.72 0.56
0.204
-0.06 0.07
0.76
0.800
0.37 0.59
0.537
-0.03 0.07
0.43
0.280
-0.15 0.48
0.756
-0.02 0.03
0.66
0.766
0.70 0.61
0.248
0.03
0.06
Table C.1: New episodes of illness model parameters for generalized estimating equation as function of enterococci
on hands and enterococci on surfaces while controlling for site. Lags of up to plus and minus seven days between
illness and contamination are modelled separately. “Lag” is the number of days between collection of health data
and the collection of data for contamination on hands, and fomites, so a positive lag implies that health outcomes
preceeded microbial contamination use and negative lags imply that health data succeeded microbial contamination.
“Obs.” refers to the number of observations with both health measurements and microbial contamination data
at the specified lag. ”Ill.” refers to the number of observations in which new episodes were observed,“Hands” is
the number of enterococci detected on an individual’s hands, “Fomites” is the fraction of surfaces sampled with
detectable enterococci, “Site” is the facility, with the coefficients representative of the difference in the model for
Site B relative to Site A, “Coef.” is the coefficient on the variable, “SE” is the standard error of the coefficent, and
“Pr(>|W|)” is the significance of the coefficient, with values less than 0.05 considered significant and highlighted
in bold.
Lag
7
6
5
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
157
Obs.
434
391
326
212
203
283
454
469
361
228
193
231
368
438
Ill.
31
19
20
7
13
17
12
20
18
9
12
17
27
25
Coef.
-2.89
-2.64
-3.05
-2.99
-3.15
-4.05
-4.03
-3.06
-2.64
-3.73
-2.39
-3.27
-2.86
-3.23
Intercept
SE Pr(>|W|)
0.37 <0.001
0.39 <0.001
0.40 <0.001
0.62 <0.001
0.57 <0.001
0.67 <0.001
0.58 <0.001
0.42 <0.001
0.43 <0.001
0.84 <0.001
0.40 <0.001
0.56 <0.001
0.33 <0.001
0.42 <0.001
Hands
Coef. SE Pr(>|W|)
-0.13 0.29
0.664
0.08 0.35
0.811
-0.05 0.32
0.876
-0.64 0.54
0.237
-0.62 0.52
0.239
0.29 0.34
0.398
-0.03 0.36
0.929
0.39 0.26
0.133
-0.93 0.51
0.068
0.47 0.39
0.228
-0.36 0.48
0.452
-0.59 0.29
0.043
0.31 0.22
0.152
-0.12 0.39
0.758
Coef.
0.53
-0.53
0.33
0.47
2.17
0.43
0.28
-0.41
-0.86
-0.26
0.83
0.09
0.45
0.34
Surfaces
SE Pr(>|W|)
0.24
0.023
0.46
0.249
0.41
0.411
0.74
0.523
0.56 <0.001
0.31
0.171
0.47
0.558
0.35
0.241
0.59
0.142
0.54
0.632
0.69
0.230
0.55
0.871
0.35
0.198
0.41
0.413
Coef.
0.37
-0.58
0.45
-0.81
-0.15
1.44
0.58
-0.54
0.59
0.65
-0.79
1.49
-0.13
0.60
Site
Correlation
SE Pr(>|W|) Estimate SE
0.44
0.401
0.05
0.04
0.51
0.254
-0.02 0.03
0.40
0.255
-0.07 0.04
0.78
0.296
0.06
0.24
0.58
0.792
-0.03 0.10
0.63
0.022
-0.01 0.06
0.62
0.356
-0.01 0.03
0.59
0.353
0.09
0.13
0.50
0.235
-0.01 0.05
0.78
0.406
0.10
0.34
0.58
0.173
-0.04 0.06
0.66
0.024
0.01
0.03
0.34
0.704
-0.07 0.05
0.40
0.136
-0.02 0.02
Table C.2: Illness-related absences model parameters for generalized estimating equation as function of enterococci
on hands and enterococci on surfaces, while controlling for site. Lags of up to plus and minus seven days between
illness and contamination are modelled separately. “Lag” is the number of days between collection of health data
and the collection of data for contamination on hands and fomites, so a positive lag implies that health outcomes
preceeded microbial contamination and negative lags imply that health data succeeded microbial contamination.
“Obs.” refers to the number of observations with both health measurements and microbial contamination data
at the specified lag. ”Ill.” refers to the number of observations in which illness-absences was observed,“Hands” is
the number of enterococci detected on an individual’s hands, “Fomites” is the fraction of surfaces sampled with
detectable enterococci, “Site” is the facility, with the coefficients representative of the difference in the model for
Site B relative to Site A, “Coef.” is the coefficient on the variable, “SE” is the standard error of the coefficent, and
“Pr(>|W|)” is the significance of the coefficient, with values less than 0.05 considered significant and highlighted
in bold.
Lag
7
6
5
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5
158
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