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Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic.
Tuncer, Necibe; Timsina, Archana; Nuno, Miriam; Chowell, Gerardo; Martcheva, Maia.
  • Tuncer N; Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA.
  • Timsina A; Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA.
  • Nuno M; Department of Biostatistics, University of California, Davis, CA, USA.
  • Chowell G; Department of Population Health Sciences, Georgia State University, Atlanta, GA, USA.
  • Martcheva M; Department of Mathematics, University of Florida, Gainesville, FL, USA.
J Biol Dyn ; 16(1): 412-438, 2022 12.
Article in English | MEDLINE | ID: covidwho-1868208
ABSTRACT
We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study Limits: Humans Language: English Journal: J Biol Dyn Journal subject: Biology Year: 2022 Document Type: Article Affiliation country: 17513758.2022.2078899

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study Limits: Humans Language: English Journal: J Biol Dyn Journal subject: Biology Year: 2022 Document Type: Article Affiliation country: 17513758.2022.2078899