A data-driven model for COVID-19 pandemic - evolution of the attack rate and prognosis for Brazil
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
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. |
id |
RCAP_dead990360292c7f8e7d941129beddc3 |
---|---|
oai_identifier_str |
oai:ria.ua.pt:10773/36658 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799137729276018688 |