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Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks

We study a multi-type SIR epidemic process within a heterogeneous population that interacts through a network. We base social contact on a random graph with given vertex degrees, and we give limit theorems on the fraction of infected individuals. For given social distancing individual strategies, we...

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Detalles Bibliográficos
Autores principales: Amini, Hamed, Minca, Andreea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8723826/
https://www.ncbi.nlm.nih.gov/pubmed/35003828
http://dx.doi.org/10.1007/s13235-021-00411-1
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author Amini, Hamed
Minca, Andreea
author_facet Amini, Hamed
Minca, Andreea
author_sort Amini, Hamed
collection PubMed
description We study a multi-type SIR epidemic process within a heterogeneous population that interacts through a network. We base social contact on a random graph with given vertex degrees, and we give limit theorems on the fraction of infected individuals. For given social distancing individual strategies, we establish the epidemic reproduction number [Formula: see text] , which can be used to identify network vulnerability and inform vaccination policies. In the second part of the paper, we study the equilibrium of the social distancing game. Individuals choose their social distancing level according to an anticipated global infection rate, which must equal the actual infection rate following their choices. We give conditions for the existence and uniqueness of an equilibrium. In the case of random regular graphs, we show that voluntary social distancing will always be socially sub-optimal.
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spelling pubmed-87238262022-01-04 Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks Amini, Hamed Minca, Andreea Dyn Games Appl Article We study a multi-type SIR epidemic process within a heterogeneous population that interacts through a network. We base social contact on a random graph with given vertex degrees, and we give limit theorems on the fraction of infected individuals. For given social distancing individual strategies, we establish the epidemic reproduction number [Formula: see text] , which can be used to identify network vulnerability and inform vaccination policies. In the second part of the paper, we study the equilibrium of the social distancing game. Individuals choose their social distancing level according to an anticipated global infection rate, which must equal the actual infection rate following their choices. We give conditions for the existence and uniqueness of an equilibrium. In the case of random regular graphs, we show that voluntary social distancing will always be socially sub-optimal. Springer US 2022-01-04 2022 /pmc/articles/PMC8723826/ /pubmed/35003828 http://dx.doi.org/10.1007/s13235-021-00411-1 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Amini, Hamed
Minca, Andreea
Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks
title Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks
title_full Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks
title_fullStr Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks
title_full_unstemmed Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks
title_short Epidemic Spreading and Equilibrium Social Distancing in Heterogeneous Networks
title_sort epidemic spreading and equilibrium social distancing in heterogeneous networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8723826/
https://www.ncbi.nlm.nih.gov/pubmed/35003828
http://dx.doi.org/10.1007/s13235-021-00411-1
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