ArticlePLoS computational biology2015
Predicting epidemic risk from past temporal contact data.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Simulating contact networks for livestock disease epidemiology: a systematic review.Journal of the Royal Society, Interface · 2023Pooled it
- Modelling plant disease spread via high-resolution human mobility networks.Journal of the Royal Society, Interface · 2026Article
- Assessing the impact of mitigation measures on cattle movement patterns in Austria: bluetongue virus outbreaks as a case study.Frontiers in veterinary science · 2026Article
- Preserving friendships in school contacts: An algorithm to construct synthetic temporal networks for epidemic modelling.PLoS computational biology · 2024Article
- Infection patterns in simple and complex contagion processes on networks.PLoS computational biology · 2024Article
- An algorithm to build synthetic temporal contact networks based on close-proximity interactions data.PLoS computational biology · 2024Article
- Article
- Identifying target areas for risk-based surveillance and control of transboundary animal diseases: a seasonal analysis of slaughter and live-trade cattle movements in Uganda.Scientific reports · 2023Article
- Uncovering chains of infections through spatio-temporal and visual analysis of COVID-19 contact traces.Computers & graphics · 2022Article
- A Metapopulation Model for Preventing the Reintroduction of Bovine Viral Diarrhea Virus to Naïve Herds: Scotland Case Study.Frontiers in veterinary science · 2022Article
- Complex network analysis to understand trading partnership in French swine production.PloS one · 2022Article
- Reconstructing contact network structure and cross-immunity patterns from multiple infection histories.PLoS computational biology · 2021Article
- Article
- The role of social structure and dynamics in the maintenance of endemic disease.Behavioral ecology and sociobiology · 2021Review
- A Temporal Network Model for Livestock Trade Systems.Frontiers in veterinary science · 2021Article
- Outbreak detection for temporal contact data.Applied network science · 2021Article
- Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus.Network neuroscience (Cambridge, Mass.) · 2020Article
- Descriptive analysis of horse movement networks during the 2015 equestrian season in Ontario, Canada.PloS one · 2019Article
- Spreading dynamics in a cattle trade network: Size, speed, typical profile and consequences on epidemic control strategies.PloS one · 2019Article
- Disease persistence on temporal contact networks accounting for heterogeneous infectious periods.Royal Society open science · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.