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Fast and principled simulations of the SIR model on temporal networks

The Susceptible–Infectious–Recovered (SIR) model is the canonical model of epidemics of infections that make people immune upon recovery. Many of the open questions in computational epidemiology concern the underlying contact structure’s impact on models like the SIR model. Temporal networks constit...

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Detalles Bibliográficos
Autor principal: Holme, Petter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880429/
https://www.ncbi.nlm.nih.gov/pubmed/33577564
http://dx.doi.org/10.1371/journal.pone.0246961
Descripción
Sumario:The Susceptible–Infectious–Recovered (SIR) model is the canonical model of epidemics of infections that make people immune upon recovery. Many of the open questions in computational epidemiology concern the underlying contact structure’s impact on models like the SIR model. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this article, we discuss the detailed assumptions behind such simulations—how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also present a highly optimized, open-source code for this purpose and discuss all steps needed to make the program as fast as possible.