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Minimizing the epidemic final size while containing the infected peak prevalence in SIR systems.
Sereno, Juan; Anderson, Alejandro; Ferramosca, Antonio; Hernandez-Vargas, Esteban A; González, Alejandro Hernán.
Afiliação
  • Sereno J; Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL), Guemes 3450, Santa Fe, 3000, Argentina.
  • Anderson A; Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL), Guemes 3450, Santa Fe, 3000, Argentina.
  • Ferramosca A; Department of Management, Information and Production Engineering, University of Bergamo, Via Marconi 5, Dalmine (BG), 24044, Italy.
  • Hernandez-Vargas EA; Instituto de Matemáticas, UNAM, Boulevard Juriquilla 3001, Querétaro, 76230, Mexico.
  • González AH; Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438, Frankfurt am Main, 76230, Germany.
Automatica (Oxf) ; 144: 110496, 2022 Oct.
Article em En | MEDLINE | ID: mdl-35936927
Mathematical models are critical to understand the spread of pathogens in a population and evaluate the effectiveness of non-pharmaceutical interventions (NPIs). A plethora of optimal strategies has been recently developed to minimize either the infected peak prevalence ( I P P ) or the epidemic final size ( E F S ). While most of them optimize a simple cost function along a fixed finite-time horizon, no consensus has been reached about how to simultaneously handle the I P P and the E F S , while minimizing the intervention's side effects. In this work, based on a new characterization of the dynamical behaviour of SIR-type models under control actions (including the stability of equilibrium sets in terms of herd immunity), we study how to minimize the E F S while keeping the I P P controlled at any time. A procedure is proposed to tailor NPIs by separating transient from stationary control objectives: the potential benefits of the strategy are illustrated by a detailed analysis and simulation results related to the COVID-19 pandemic.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prevalence_studies / Risk_factors_studies Idioma: En Revista: Automatica (Oxf) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Argentina País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prevalence_studies / Risk_factors_studies Idioma: En Revista: Automatica (Oxf) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Argentina País de publicação: Reino Unido