Predicting Epidemic Risk from Past Temporal Contact Data
PLOS Computational Biology, 11 (3):
e1004152+ (Mar 12, 2015)DOI: 10.1371/journal.pcbi.1004152
Abstract
Understanding how epidemics spread in a system is a crucial step to prevent
and control outbreaks, with broad implications on the system's functioning,
health, and associated epidemic costs. This can be achieved by identifying the
elements at higher risk of infection and implementing targeted surveillance and
control measures. One important ingredient to consider is the pattern of
disease-transmission contacts among the elements, however lack of data or
possible delays in providing updated records may hinder its use, especially for
time-varying patterns. Here we explore to what extent it is possible to use
past temporal data of a system's pattern of contacts to predict the risk of
infection of its elements during an emerging outbreak, in absence of updated
data. We focus on two real-world temporal systems; a livestock displacements
trade network among animal holdings, and a network of sexual encounters in
high-end prostitution. We define the node's loyalty as a local measure of the
tendency to maintain contacts with the same elements over time, and uncover
important non-trivial correlations with the node's epidemic risk. We show that
a risk assessment analysis incorporating this knowledge and based on past
structural and temporal pattern properties provide accurate predictions for
both systems. Its generalizability is tested by introducing a theoretical model
for generating synthetic temporal networks. High accuracy is recovered across
variations of the system's features, whereas the predictive power is found to
be system-specific. The proposed method can provide crucial information for the
setup of targeted intervention strategies.