Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/26281
Title: | COVID-19: Analytics of contagion on inhomogeneous random social networks |
Authors: | Hurd TR |
Publication Date: | 2021 |
Publisher: | Elsevier BV |
Citation: | Hurd, T. R. “COVID-19: Analytics of Contagion on Inhomogeneous Random Social Networks.” Infectious Disease Modelling, vol. 6, Jan. 2021, pp. 75–90, doi:10.1016/j.idm.2020.11.001. |
Abstract: | Motivated by the need for robust models of the Covid-19 epidemic that adequately reflect the extreme heterogeneity of humans and society, this paper presents a novel framework that treats a population of N individuals as an inhomogeneous random social network (IRSN). The nodes of the network represent individuals of different types and the edges represent significant social relationships. An epidemic is pictured as a contagion process that develops day by day, triggered by a seed infection introduced into the population on day 0. Individuals’ social behaviour and health status are assumed to vary randomly within each type, with probability distributions that vary with their type. A formulation and analysis is given for a SEIR (susceptible-exposed-infective-removed) network contagion model, considered as an agent based model, which focusses on the number of people of each type in each compartment each day. The main result is an analytical formula valid in the large N limit for the stochastic state of the system on day t in terms of the initial conditions. The formula involves only one-dimensional integration. The model can be implemented numerically for any number of types by a deterministic algorithm that efficiently incorporates the discrete Fourier transform. While the paper focusses on fundamental properties rather than far ranging applications, a concluding discussion addresses a number of domains, notably public awareness, infectious disease research and public health policy, where the IRSN framework may provide unique insights. |
metadata.dc.rights.license: | Attribution-NonCommercial-NoDerivs - CC BY-NC-ND |
Rights: | Attribution-NonCommercial-NoDerivs - CC BY-NC-ND This license is the most restrictive of the main Creative Commons licenses, only allowing others to download your works and share them with others as long as they credit you, but they can?t change them in any way or use them commercially. |
URI: | http://hdl.handle.net/11375/26281 |
metadata.dc.identifier.doi: | https://doi.org/10.1016/j.idm.2020.11.001 |
ISSN: | 2468-0427 |
Appears in Collections: | Mathematics & Statistics Publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S2468042720300713-main.pdf | Published version | 1.11 MB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.