Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis

Bo Li and David Saad
Phys. Rev. E 103, 052303 – Published 6 May 2021

Abstract

Infectious diseases that incorporate presymptomatic transmission are challenging to monitor, model, predict, and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic.

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  • Received 2 November 2020
  • Accepted 19 April 2021

DOI:https://doi.org/10.1103/PhysRevE.103.052303

©2021 American Physical Society

Physics Subject Headings (PhySH)

NetworksInterdisciplinary PhysicsStatistical Physics & Thermodynamics

Authors & Affiliations

Bo Li* and David Saad

  • Non-linearity and Complexity Research Group, Aston University, Birmingham, B4 7ET, United Kingdom

  • *b.li10@aston.ac.uk
  • d.saad@aston.ac.uk

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Issue

Vol. 103, Iss. 5 — May 2021

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