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A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling

Motivated by modelling epidemics like COVID-19, this paper proposes a generalized chain binomial process which integrates two types of infectives, those with symptoms and those without. Testing of infectives and vaccination of susceptibles are then incorporated as preventive protective measures. Our...

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Detalles Bibliográficos
Autores principales: Lefèvre, Claude, Picard, Philippe, Simon, Matthieu, Utev, Sergey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559945/
https://www.ncbi.nlm.nih.gov/pubmed/34725739
http://dx.doi.org/10.1007/s00285-021-01680-5
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author Lefèvre, Claude
Picard, Philippe
Simon, Matthieu
Utev, Sergey
author_facet Lefèvre, Claude
Picard, Philippe
Simon, Matthieu
Utev, Sergey
author_sort Lefèvre, Claude
collection PubMed
description Motivated by modelling epidemics like COVID-19, this paper proposes a generalized chain binomial process which integrates two types of infectives, those with symptoms and those without. Testing of infectives and vaccination of susceptibles are then incorporated as preventive protective measures. Our interest relates to the distribution of the state of the population at the end of infection and to the reproduction number [Formula: see text] with the associated extinction condition. The method uses the construction of a family of martingales and a branching approximation for large populations, respectively. A more general branching process for epidemics is also constructed and studied. Finally, some results obtained are illustrated by numerical examples.
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spelling pubmed-85599452021-11-02 A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling Lefèvre, Claude Picard, Philippe Simon, Matthieu Utev, Sergey J Math Biol Article Motivated by modelling epidemics like COVID-19, this paper proposes a generalized chain binomial process which integrates two types of infectives, those with symptoms and those without. Testing of infectives and vaccination of susceptibles are then incorporated as preventive protective measures. Our interest relates to the distribution of the state of the population at the end of infection and to the reproduction number [Formula: see text] with the associated extinction condition. The method uses the construction of a family of martingales and a branching approximation for large populations, respectively. A more general branching process for epidemics is also constructed and studied. Finally, some results obtained are illustrated by numerical examples. Springer Berlin Heidelberg 2021-11-01 2021 /pmc/articles/PMC8559945/ /pubmed/34725739 http://dx.doi.org/10.1007/s00285-021-01680-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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
Lefèvre, Claude
Picard, Philippe
Simon, Matthieu
Utev, Sergey
A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling
title A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling
title_full A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling
title_fullStr A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling
title_full_unstemmed A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling
title_short A chain binomial epidemic with asymptomatics motivated by COVID-19 modelling
title_sort chain binomial epidemic with asymptomatics motivated by covid-19 modelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559945/
https://www.ncbi.nlm.nih.gov/pubmed/34725739
http://dx.doi.org/10.1007/s00285-021-01680-5
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