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Modelling and optimal control of multi strain epidemics, with application to COVID-19.
Arruda, Edilson F; Das, Shyam S; Dias, Claudia M; Pastore, Dayse H.
  • Arruda EF; Department of Decision Analytics and Risk, Southampton Business School, University of Southampton, Southampton, United Kingdom.
  • Das SS; Graduate Program in Mathematical and Computational Modeling, Multidisciplinary Institute, Federal Rural University of Rio de Janeiro, Nova Iguaçu RJ, Brazil.
  • Dias CM; Graduate Program in Mathematical and Computational Modeling, Multidisciplinary Institute, Federal Rural University of Rio de Janeiro, Nova Iguaçu RJ, Brazil.
  • Pastore DH; Department of Basic and General Disciplines, Federal Center for Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Rio de Janeiro, Brazil.
PLoS One ; 16(9): e0257512, 2021.
Artigo em Inglês | MEDLINE | ID: covidwho-1416904
ABSTRACT
Reinfection and multiple viral strains are among the latest challenges in the current COVID-19 pandemic. In contrast, epidemic models often consider a single strain and perennial immunity. To bridge this gap, we present a new epidemic model that simultaneously considers multiple viral strains and reinfection due to waning immunity. The model is general, applies to any viral disease and includes an optimal control formulation to seek a trade-off between the societal and economic costs of mitigation. We validate the model, with and without mitigation, in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil. The model can derive optimal mitigation strategies for any number of viral strains, whilst also evaluating the effect of distinct mitigation costs on the infection levels. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, and therefore demand more restrictive measures in the future.
Assuntos

Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Algoritmos / Viroses / COVID-19 / Modelos Teóricos Tipo de estudo: Estudo experimental / Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: América do Sul / Brasil / Europa Idioma: Inglês Revista: PLoS One Assunto da revista: Ciência / Medicina Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: Journal.pone.0257512

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Algoritmos / Viroses / COVID-19 / Modelos Teóricos Tipo de estudo: Estudo experimental / Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: América do Sul / Brasil / Europa Idioma: Inglês Revista: PLoS One Assunto da revista: Ciência / Medicina Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: Journal.pone.0257512