Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states

P. Yarsky

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 185, issue C, 687-695

Abstract: A Susceptible–Exposed–Infected–Removed​ (SEIR) model was developed to forecast the spread of the novel coronavirus (SARS-CoV-2) in the United States and the implications of re-opening and hospital resource utilization. The model relies on the specification of various parameters that characterize the virus and the population being modeled. However, several of these parameters can be expected to vary significantly between states. Therefore, a genetic algorithm was developed that adjusts these population-dependent parameters to fit the SEIR model to data for any given state.

Keywords: SARS-CoV-2; COVID-19; SEIR model; Genetic algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:185:y:2021:i:c:p:687-695

DOI: 10.1016/j.matcom.2021.01.022

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