Exact and approximate formulas for contact tracing on random trees.

Branching process Contact tracing Message passing model Network Stochastic SIR model Tree

Journal

Mathematical biosciences
ISSN: 1879-3134
Titre abrégé: Math Biosci
Pays: United States
ID NLM: 0103146

Informations de publication

Date de publication:
03 2020
Historique:
received: 16 10 2019
revised: 23 01 2020
accepted: 23 01 2020
pubmed: 6 2 2020
medline: 3 11 2020
entrez: 5 2 2020
Statut: ppublish

Résumé

We consider a stochastic susceptible-infected-recovered (SIR) model with contact tracing on random trees and on the configuration model. On a rooted tree, where initially all individuals are susceptible apart from the root which is infected, we are able to find exact formulas for the distribution of the infectious period. Thereto, we show how to extend the existing theory for contact tracing in homogeneously mixing populations to trees. Based on these formulas, we discuss the influence of randomness in the tree and the basic reproduction number. We find the well known results for the homogeneously mixing case as a limit of the present model (tree-shaped contact graph). Furthermore, we develop approximate mean field equations for the dynamics on trees, and - using the message passing method - also for the configuration model. The interpretation and implications of the results are discussed.

Identifiants

pubmed: 32014418
pii: S0025-5564(20)30015-8
doi: 10.1016/j.mbs.2020.108320
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

108320

Commentaires et corrections

Type : ErratumIn

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

Auteurs

Augustine Okolie (A)

Center for Mathematical Sciences, Technische Universität München, Garching 85748, Germany. Electronic address: augustine.okolie@tum.de.

Johannes Müller (J)

Center for Mathematical Sciences, Technische Universität München, Garching 85748, Germany; Institute for Computational Biology, Helmholtz Center Munich, Neuherberg 85764, Germany.

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Classifications MeSH