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

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.

Publication DOI: https://doi.org/10.1103/PhysRevE.103.052303
Divisions: College of Engineering & Physical Sciences > Mathematics
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: ©2021 American Physical Society Funding: B.L. and D.S. acknowledge support from European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No.~835913. D.S. acknowledges support from the EPSRC program grant TRANSNET (EP/R035342/1).
Uncontrolled Keywords: epidemic spreading,contact networks,dynamic message-passing,presymptomatic transmission,epidemic threshold,nonbacktracking centrality,Covid-19
Full Text Link: https://arxiv.o ... /abs/2010.14598
Related URLs: https://journal ... RevE.103.052303 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-05-06
Accepted Date: 2021-04-19
Authors: Li, Bo (ORCID Profile 0000-0001-9743-9447)
Saad, David (ORCID Profile 0000-0001-9821-2623)

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record