A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil

Detalhes bibliográficos
Autor(a) principal: Rocha Filho, T. M.
Data de Publicação: 2021
Outros Autores: Moret, M. A., Chow, C. C., Phillips, J. C., Cordeiro, A. J. A., Scorza, F. A., Almeida, A-.C. G., Mendes, J. F. F.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/36658
Resumo: We introduce a compartmental model SEIAHRV (Susceptible, Exposed, Infected, Asymptomatic, Hospitalized, Recovered, Vaccinated) with age structure for the spread of the SARAS-CoV virus. In order to model current different vaccines we use compartments for individuals vaccinated with one and two doses without vaccine failure and a compartment for vaccinated individual with vaccine failure. The model allows to consider any number of different vaccines with different efficacies and delays between doses. Contacts among age groups are modeled by a contact matrix and the contagion matrix is obtained from a probability of contagion pc per contact. The model uses known epidemiological parameters and the time dependent probability pc is obtained by fitting the model output to the series of deaths in each locality, and reflects non-pharmaceutical interventions. As a benchmark the output of the model is compared to two good quality serological surveys, and applied to study the evolution of the COVID-19 pandemic in the main Brazilian cities with a total population of more than one million. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of We also estimate the attack rate, the total proportion of cases (symptomatic and asymptomatic) with respect to the total population, for all Brazilian states since the beginning of the COVID-19 pandemic. We argue that the model present here is relevant to assessing present policies not only in Brazil but also in any place where good serological surveys are not available.
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spelling A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for BrazilEpidemiological modelNon-linear ODESARS-CoV-2COVID-19We introduce a compartmental model SEIAHRV (Susceptible, Exposed, Infected, Asymptomatic, Hospitalized, Recovered, Vaccinated) with age structure for the spread of the SARAS-CoV virus. In order to model current different vaccines we use compartments for individuals vaccinated with one and two doses without vaccine failure and a compartment for vaccinated individual with vaccine failure. The model allows to consider any number of different vaccines with different efficacies and delays between doses. Contacts among age groups are modeled by a contact matrix and the contagion matrix is obtained from a probability of contagion pc per contact. The model uses known epidemiological parameters and the time dependent probability pc is obtained by fitting the model output to the series of deaths in each locality, and reflects non-pharmaceutical interventions. As a benchmark the output of the model is compared to two good quality serological surveys, and applied to study the evolution of the COVID-19 pandemic in the main Brazilian cities with a total population of more than one million. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of We also estimate the attack rate, the total proportion of cases (symptomatic and asymptomatic) with respect to the total population, for all Brazilian states since the beginning of the COVID-19 pandemic. We argue that the model present here is relevant to assessing present policies not only in Brazil but also in any place where good serological surveys are not available.Elsevier2021-112021-11-01T00:00:00Z2023-11-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/36658eng0960-077910.1016/j.chaos.2021.111359Rocha Filho, T. M.Moret, M. A.Chow, C. C.Phillips, J. C.Cordeiro, A. J. A.Scorza, F. A.Almeida, A-.C. G.Mendes, J. F. F.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:10:43Zoai:ria.ua.pt:10773/36658Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:07:24.363390Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
title A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
spellingShingle A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
Rocha Filho, T. M.
Epidemiological model
Non-linear ODE
SARS-CoV-2
COVID-19
title_short A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
title_full A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
title_fullStr A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
title_full_unstemmed A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
title_sort A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
author Rocha Filho, T. M.
author_facet Rocha Filho, T. M.
Moret, M. A.
Chow, C. C.
Phillips, J. C.
Cordeiro, A. J. A.
Scorza, F. A.
Almeida, A-.C. G.
Mendes, J. F. F.
author_role author
author2 Moret, M. A.
Chow, C. C.
Phillips, J. C.
Cordeiro, A. J. A.
Scorza, F. A.
Almeida, A-.C. G.
Mendes, J. F. F.
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Rocha Filho, T. M.
Moret, M. A.
Chow, C. C.
Phillips, J. C.
Cordeiro, A. J. A.
Scorza, F. A.
Almeida, A-.C. G.
Mendes, J. F. F.
dc.subject.por.fl_str_mv Epidemiological model
Non-linear ODE
SARS-CoV-2
COVID-19
topic Epidemiological model
Non-linear ODE
SARS-CoV-2
COVID-19
description We introduce a compartmental model SEIAHRV (Susceptible, Exposed, Infected, Asymptomatic, Hospitalized, Recovered, Vaccinated) with age structure for the spread of the SARAS-CoV virus. In order to model current different vaccines we use compartments for individuals vaccinated with one and two doses without vaccine failure and a compartment for vaccinated individual with vaccine failure. The model allows to consider any number of different vaccines with different efficacies and delays between doses. Contacts among age groups are modeled by a contact matrix and the contagion matrix is obtained from a probability of contagion pc per contact. The model uses known epidemiological parameters and the time dependent probability pc is obtained by fitting the model output to the series of deaths in each locality, and reflects non-pharmaceutical interventions. As a benchmark the output of the model is compared to two good quality serological surveys, and applied to study the evolution of the COVID-19 pandemic in the main Brazilian cities with a total population of more than one million. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of We also estimate the attack rate, the total proportion of cases (symptomatic and asymptomatic) with respect to the total population, for all Brazilian states since the beginning of the COVID-19 pandemic. We argue that the model present here is relevant to assessing present policies not only in Brazil but also in any place where good serological surveys are not available.
publishDate 2021
dc.date.none.fl_str_mv 2021-11
2021-11-01T00:00:00Z
2023-11-30T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/36658
url http://hdl.handle.net/10773/36658
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0960-0779
10.1016/j.chaos.2021.111359
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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