Predicting epidemic risk from past temporal contact data

PLoS Computational Biology
Eugenio ValdanoVittoria Colizza

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 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 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 its 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 propert...Continue Reading

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Citations

Mar 18, 2016·Journal of the Royal Society, Interface·Mathieu Moslonka-LefebvreElisabeta Vergu
Jan 27, 2017·PLoS Computational Biology·Gianluigi RossiLuca Bolzoni
Jul 27, 2017·PLoS Computational Biology·Christopher Finn McQuaidChristopher Aidan Gilligan
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Feb 23, 2021·Network Neuroscience·Nicola PedreschiDemian Battaglia
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Mar 9, 2021·Applied Network Science·Martin SterchiAbraham Bernstein
Aug 24, 2021·Behavioral Ecology and Sociobiology·Matthew J Silk, Nina H Fefferman
Sep 16, 2021·PLoS Computational Biology·Christian Selinger, Samuel Alizon

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