title: Predicting epidemic risk from past temporal contact data.
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 properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.
authors.affiliation: INSERM, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 56 bd Vincent Auriol-CS 81393-75646 Paris Cedex 13, France; Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 56 bd Vincent Auriol-CS 81393 - 75646 Paris Cedex 13, France.
authors.forename: Eugenio
authors.initials: E
authors.lastname: Valdano
authors.affiliation: INSERM, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 56 bd Vincent Auriol-CS 81393-75646 Paris Cedex 13, France; Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 56 bd Vincent Auriol-CS 81393 - 75646 Paris Cedex 13, France.
authors.forename: Chiara
authors.initials: C
authors.lastname: Poletto
authors.affiliation: Istituto Zooprofilattico Sperimentale Abruzzo-Molise G. Caporale Campo Boario, 64100 Teramo, Italy.
authors.forename: Armando
authors.initials: A
authors.lastname: Giovannini
authors.affiliation: Istituto Zooprofilattico Sperimentale Abruzzo-Molise G. Caporale Campo Boario, 64100 Teramo, Italy.
authors.forename: Diana
authors.initials: D
authors.lastname: Palma
authors.affiliation: Istituto Zooprofilattico Sperimentale Abruzzo-Molise G. Caporale Campo Boario, 64100 Teramo, Italy.
authors.forename: Lara
authors.initials: L
authors.lastname: Savini
authors.affiliation: INSERM, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 56 bd Vincent Auriol-CS 81393-75646 Paris Cedex 13, France; Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 56 bd Vincent Auriol-CS 81393 - 75646 Paris Cedex 13, France; ISI Foundation Via Alassio 11/c, 10126 Torino, Italy.
authors.forename: Vittoria
authors.initials: V
authors.lastname: Colizza
mesh_terms: D000818:Animals; D002417:Cattle; D002418:Cattle Diseases / Q000453:epidemiology; D003141:Communicable Diseases / Q000453:epidemiology*; D019295:Computational Biology / Q000379:methods*; D016358:Contact Tracing / Q000379:methods*; D016208:Databases, Factual; D058872:Epidemics / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological; D018570:Risk Assessment; D011477:Sex Work / Q000706:statistics & numerical data; D013997:Time Factors