Influenza: Understanding and predicting disease patterns and the impact of control
measures
Thomas House, Leon Danon, Nadia Inglis, Matt Keeling
Mathematics Institute and School of Life Sciences, University of Warwick, Coventry
Health Protection Agency West Midlands, Birmingham
Mathematical models have been used extensively for exploring and predicting
Figure 3. Age distribution of laboratory-confirmed cases of (H1N1) 2009 and their close
contacts during the early stage of the pandemic in 628 households in Birmingham
patterns of spread of influenza, given the potential of the infection to cause
pandemics, of which several in the past century have led to widespread mortality.
Figure 1 displays a model which considers how infection transmission may vary
between wards in Great Britain, based on information about household sizes and
the number of dependent children in households. 1 R0 is the basic reproductive
number, i.e. the average number of people infected by one infected individual
(assuming all individuals are susceptible). This important number reflects the rate at
which infection spreads.
Figure 1. Distribution of R0 values in Great Britain by ward, according to household size
and number of dependent children in households
Understanding infectious disease transmission patterns can be used to predict the
impact of control measures upon an epidemic, and more specifically health service
demand. These predictions can, in turn, help to guide management strategies. Figure
4 shows the predicted impact of school closures upon Intensive Care Unit capacity
pressure (a,b) in England, and the distance necessarily travelled by both adults and
children to a hospital with spare capacity (c,d). 2
Figure 4. Effect of school closures on ICU capacity pressure according to percentage of
schools closed
School closures reduce local
peak by:
15%(Red lines)
30%(Green lines)
60% (Blue lines)
Initial work carried out to develop an understanding of how pandemic (H1N1) 2009
unfolded in the West Midlands, shows that an early large outbreak in a primary school
National adult ICU demand
as a percentage of capacity:
in the Birmingham area was followed closely by the first pandemic wave (Figure 2).
The initial transmission of the virus among school-aged children is reflected in Figure
150% (dashed lines – more
severe epidemic)
100% (solid lines)
67% (dashed-dotted line)
3. Further work is being undertaken to explore how infection was transmitted within
households in Birmingham, an area disproportionately affected during the pandemic.
Figure 2. Epidemic curve showing number of laboratory-confirmed cases in the West
Midlands in those with a recorded date of illness onset from April 16 to July 2, 2009 (n = 1,962)
Figure 5 demonstrates a model constructed to identify optimal strategies for
vaccinating the population against influenza. It helps to answer an important question
160
related to whether greater benefit can be achieved through vaccinating individuals at
Number of laboratory-confirmed cases
140
19 June: Areas in Birmingham declared as hotspots
high risk of complications, or those who are responsible for high rates of transmission
12 June: 43 schools in
total with confirmed
cases
120
(Figure 5).3
Central Birmingham
Primary School
9 June: 17 more
schools with
confirmed cases
100
Secondary/Tertiary/Not
stated
2 July: 209
schools with
confirmed
cases
80
60
30 April: First
West Midands
travel related
case reported
27 April: First
cases in UK
reported
40
20
18 May: HPA notified
of increased
absenteeismat
primary school in
central Birmingham
1 June: Central
Birmingham
primary school
re-opened
Travel-related
Community/Sporadic
2009 Jul 02
2009 Jun 25
2009 Jun 18
2009 Jun 11
2009 Jun 04
2009 May 28
2009 May 21
2009 May 14
2009 Apr 30
2009 May 07
2009 Apr 23
2009 Apr 16
0
Date of illness onset
References
1. House T, Keeling MJ. Household Structure and Infectious Disease Transmission. Epidemiol.
Infect. (2009), 137, 654–661.
2. House T, Baguelin M, Van Hoek A J, White P J, Sadique Z, Eames, K et al. Modelling the
impact of local reactive school closures on critical care provision during an influenza pandemic.
Proc Biol Sci. 2011. doi: 10.1098/rspb.2010.2688
3. Keeling MJ, White PJ. Targeting vaccination against novel infections: risk, age and spatial
structure for pandemic influenza in Great Britain. J R Soc Interface. 2011; 8(58): 661-70
Figure 5. Epidemiological trade-off between initially vaccinating dominant transmitters
compared with those likely to suffer serious consequences of infection