Cargando…

Competing control scenarios in probabilistic SIR epidemics on social-contact networks

A probabilistic approach to the epidemic evolution on realistic social-contact networks allows for characteristic differences among subjects, including the individual number and structure of social contacts, and the heterogeneity of the infection and recovery rates according to age or medical precon...

Descripción completa

Detalles Bibliográficos
Autores principales: Broekaert, Jan B., La Torre, Davide, Hafiz, Faizal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581457/
https://www.ncbi.nlm.nih.gov/pubmed/36281317
http://dx.doi.org/10.1007/s10479-022-05031-5
_version_ 1784812630643310592
author Broekaert, Jan B.
La Torre, Davide
Hafiz, Faizal
author_facet Broekaert, Jan B.
La Torre, Davide
Hafiz, Faizal
author_sort Broekaert, Jan B.
collection PubMed
description A probabilistic approach to the epidemic evolution on realistic social-contact networks allows for characteristic differences among subjects, including the individual number and structure of social contacts, and the heterogeneity of the infection and recovery rates according to age or medical preconditions. Within our probabilistic Susceptible-Infectious-Removed (SIR) model on social-contact networks, we evaluate the infection load or activation margin of various control scenarios; by confinement, by vaccination, and by their combination. We compare the epidemic burden for subpopulations that apply competing or cooperative control strategies. The simulation experiments are conducted on randomized social-contact graphs that are designed to exhibit realistic person–person contact characteristics and which follow near homogeneous or block-localized subpopulation spreading. The scalarization method is used for the multi-objective optimization problem in which both the infection load is minimized and the extent to which each subpopulation’s control strategy preference ranking is adhered to is maximized. We obtain the compounded payoff matrices for two subpopulations that impose contrasting control strategies, each according to their proper ranked control strategy preferences. The Nash equilibria, according to each subpopulation’s compounded objective, and according to their proper ranking intensity, are discussed. Finally, the interaction effects of the control strategies are discussed and related to the type of spreading of the two subpopulations.
format Online
Article
Text
id pubmed-9581457
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-95814572022-10-20 Competing control scenarios in probabilistic SIR epidemics on social-contact networks Broekaert, Jan B. La Torre, Davide Hafiz, Faizal Ann Oper Res Original Research A probabilistic approach to the epidemic evolution on realistic social-contact networks allows for characteristic differences among subjects, including the individual number and structure of social contacts, and the heterogeneity of the infection and recovery rates according to age or medical preconditions. Within our probabilistic Susceptible-Infectious-Removed (SIR) model on social-contact networks, we evaluate the infection load or activation margin of various control scenarios; by confinement, by vaccination, and by their combination. We compare the epidemic burden for subpopulations that apply competing or cooperative control strategies. The simulation experiments are conducted on randomized social-contact graphs that are designed to exhibit realistic person–person contact characteristics and which follow near homogeneous or block-localized subpopulation spreading. The scalarization method is used for the multi-objective optimization problem in which both the infection load is minimized and the extent to which each subpopulation’s control strategy preference ranking is adhered to is maximized. We obtain the compounded payoff matrices for two subpopulations that impose contrasting control strategies, each according to their proper ranked control strategy preferences. The Nash equilibria, according to each subpopulation’s compounded objective, and according to their proper ranking intensity, are discussed. Finally, the interaction effects of the control strategies are discussed and related to the type of spreading of the two subpopulations. Springer US 2022-10-19 /pmc/articles/PMC9581457/ /pubmed/36281317 http://dx.doi.org/10.1007/s10479-022-05031-5 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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 Original Research
Broekaert, Jan B.
La Torre, Davide
Hafiz, Faizal
Competing control scenarios in probabilistic SIR epidemics on social-contact networks
title Competing control scenarios in probabilistic SIR epidemics on social-contact networks
title_full Competing control scenarios in probabilistic SIR epidemics on social-contact networks
title_fullStr Competing control scenarios in probabilistic SIR epidemics on social-contact networks
title_full_unstemmed Competing control scenarios in probabilistic SIR epidemics on social-contact networks
title_short Competing control scenarios in probabilistic SIR epidemics on social-contact networks
title_sort competing control scenarios in probabilistic sir epidemics on social-contact networks
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581457/
https://www.ncbi.nlm.nih.gov/pubmed/36281317
http://dx.doi.org/10.1007/s10479-022-05031-5
work_keys_str_mv AT broekaertjanb competingcontrolscenariosinprobabilisticsirepidemicsonsocialcontactnetworks
AT latorredavide competingcontrolscenariosinprobabilisticsirepidemicsonsocialcontactnetworks
AT hafizfaizal competingcontrolscenariosinprobabilisticsirepidemicsonsocialcontactnetworks