Patterns of social interaction and the spread of infectious disease
Thomas House, Leon Danon, Matt Keeling, Jonathan Read
Mathematics Institute and School of Life Sciences, University of Warwick, Coventry
Department of Epidemiology & Population Health, Institute of Infection and Global Health, University of Liverpool
Figure 3a. Proportion of transitive links* according to social context
Human social behaviour is an extremely important determinant of infectious disease
Figure 3b. Proportion of transitive links* according to distance from an individual’s home
transmission. Mathematical infectious disease modelling relies on assumptions
a)
b)
made about the social contact patterns of populations and population sub-groups. It
is therefore crucial to develop a detailed understanding of the structure and
characteristics of social networks at a whole population level, i.e. to understand how
people are connected and the nature of these connections.
Figure 1. Examples of individual networks described in survey
* Links between contacts of an individual. Theoretical maximum is used as denominator of
proportions.
The infection transmission implications of the social network structures defined by
survey participants was examined by assuming a randomly chosen contact is infected
(and the remainder are susceptible), and simulating how infection will be transmitted
school-girl
(12 yrs)
through the network. Three network models are compared: a simple (unweighted,
female flight attendant (22 yrs)
male fire-fighter (44 yrs)
retired male (62yrs)
unclustered) network, a weighted network accounting for duration of contact, and a
clustered weighted static network (taking into account how infection transmission
A cross-sectional study of households in Great Britain was conducted to help answer
differs in clusters). Figure 4a shows the mean number of secondary cases per infected
these questions.1 A paper-based survey was sent to randomly selected households in
participant. Figure 4b shows the overall distribution of secondary cases. Figures 4C-F
Great Britain in 2009, to be completed by one member of each household. Further
show the overall distributions for the four examples in Figure 1.
participants were recruited via a similar freely accessible online survey. Information
was collected from a total of 5,388 individuals (4,217 from the UK), describing
145,329 secondary contacts. Participants were asked to record all their social
Figure 4:. Epidemiological implications of the network structure for an infection with a latent
period of 3 days, and infectious period of 3 days and a transmission rate of 0.1 per hour across
a network connection.
contacts for a single day (Figure 1), including the intimacy, context, location, duration
and frequency of these contacts. Clustering in the networks was investigated by
asking individuals about links between their contacts. Figure 2a considers the
distribution of the number of contacts reported and an individual-based model fitted to
this. Figure 2b considers how this distribution varies according to distance of contacts
from an individual’s home.
Figure 2a. Distribution of number of contacts and individual-based model.
a)
Figure 2b. Distribution of number of contacts by distance
b)
The weighted and cluster weighted models show reduced frequencies of higher
numbers of secondary cases. The former leads to many more situations where no
secondary cases are generated and the latter increases the probability that at least
one secondary case is generated.
A high degree of clustering was found in the networks, and people with a large
These findings need validation in other settings, given the overall response rate of
number of social links tended to be contacts of other people who also had large social
3.5% to the postal survey, and similar demographic biases to those reported by other
contact networks. Highest clustering was found among contacts made in similar
health surveys. However, they highlight the need for epidemic models to consider high
contexts, although there was also significant clustering which occurred across social
levels of clustering, the impact of duration of contact and the fact that individuals tend
context boundaries (Figure 3a). Higher clustering was also found among contacts
to mix with others who have similar numbers of contacts to themselves. These have
made over 50 miles away from an individual’s home (Figure 3b). Clustering is
important implications for predictive infectious disease models and for the
important, as it tends to slow the transmission of infection.
epidemiological management of outbreaks.
References
1. Danon L, House T, Keeling MJ, Read JM. Collective properties of social encounter networks.
2011. Submitted (Science)