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Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation

Since March 11th, 2020, COVID-19 has been a global pandemic for more than one years due to a long and infectious incubation period. This paper establishes a heterogeneous epidemic model that divides the incubation period into infectious and non-infectious and employs the Bayesian framework to model...

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Autores principales: Wen, Conghua, Wei, Junwei, Ma, Zheng Feei, He, Mu, Zhao, Shi, Ji, Jiayu, He, Daihai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906904/
https://www.ncbi.nlm.nih.gov/pubmed/35287302
http://dx.doi.org/10.1016/j.idm.2022.02.001
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author Wen, Conghua
Wei, Junwei
Ma, Zheng Feei
He, Mu
Zhao, Shi
Ji, Jiayu
He, Daihai
author_facet Wen, Conghua
Wei, Junwei
Ma, Zheng Feei
He, Mu
Zhao, Shi
Ji, Jiayu
He, Daihai
author_sort Wen, Conghua
collection PubMed
description Since March 11th, 2020, COVID-19 has been a global pandemic for more than one years due to a long and infectious incubation period. This paper establishes a heterogeneous epidemic model that divides the incubation period into infectious and non-infectious and employs the Bayesian framework to model the ‘Diamond Princess’ enclosed space incident. The heterogeneity includes two different identities, two transmission methods, two different-size rooms, and six transmission stages. This model is also applicable to similar mixed structures, including closed schools, hospitals, and communities. As the COVID-19 pandemic continues, our mathematical modeling can provide management insights to the governments and policymakers on how the COVID-19 disease has spread and what prevention strategies still need to be taken.
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spelling pubmed-89069042022-03-10 Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation Wen, Conghua Wei, Junwei Ma, Zheng Feei He, Mu Zhao, Shi Ji, Jiayu He, Daihai Infect Dis Model Original Research Article Since March 11th, 2020, COVID-19 has been a global pandemic for more than one years due to a long and infectious incubation period. This paper establishes a heterogeneous epidemic model that divides the incubation period into infectious and non-infectious and employs the Bayesian framework to model the ‘Diamond Princess’ enclosed space incident. The heterogeneity includes two different identities, two transmission methods, two different-size rooms, and six transmission stages. This model is also applicable to similar mixed structures, including closed schools, hospitals, and communities. As the COVID-19 pandemic continues, our mathematical modeling can provide management insights to the governments and policymakers on how the COVID-19 disease has spread and what prevention strategies still need to be taken. KeAi Publishing 2022-03-10 /pmc/articles/PMC8906904/ /pubmed/35287302 http://dx.doi.org/10.1016/j.idm.2022.02.001 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Research Article
Wen, Conghua
Wei, Junwei
Ma, Zheng Feei
He, Mu
Zhao, Shi
Ji, Jiayu
He, Daihai
Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation
title Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation
title_full Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation
title_fullStr Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation
title_full_unstemmed Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation
title_short Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation
title_sort heterogeneous epidemic modelling within an enclosed space and corresponding bayesian estimation
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906904/
https://www.ncbi.nlm.nih.gov/pubmed/35287302
http://dx.doi.org/10.1016/j.idm.2022.02.001
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