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Forecasting emergence of COVID-19 variants of concern.
Miller, James Kyle; Elenberg, Kimberly; Dubrawski, Artur.
  • Miller JK; Auton Systems LLC, Pittsburgh, PA, United States of America.
  • Elenberg K; United States Department of Defense Covid Task Force, Washington, DC, United States of America.
  • Dubrawski A; Auton Systems LLC, Pittsburgh, PA, United States of America.
PLoS One ; 17(2): e0264198, 2022.
Article in English | MEDLINE | ID: covidwho-1703502
ABSTRACT
We consider whether one can forecast the emergence of variants of concern in the SARS-CoV-2 outbreak and similar pandemics. We explore methods of population genetics and identify key relevant principles in both deterministic and stochastic models of spread of infectious disease. Finally, we demonstrate that fitness variation, defined as a trait for which an increase in its value is associated with an increase in net Darwinian fitness if the value of other traits are held constant, is a strong indicator of imminent transition in the viral population.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Forecasting / SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0264198

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Forecasting / SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2022 Document Type: Article Affiliation country: Journal.pone.0264198