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

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  • Yarsky, P.

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.

Suggested Citation

  • Yarsky, P., 2021. "Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 687-695.
    • Handle: RePEc:eee:matcom:v:185:y:2021:i:c:p:687-695
    DOI: 10.1016/j.matcom.2021.01.022
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    Citations

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    Cited by:

    1. Ramasamy, Valarmathi & Kannan, Ramkumar & Muralidharan, Guruprasath & Sidharthan, Rakesh Kumar & Amirtharajan, Rengarajan, 2022. "Two-tier search space optimisation technique for tuning of explicit plant-model mismatch in model predictive controller for industrial cement kiln process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 385-408.
    2. Prem Kumar, R. & Santra, P.K. & Mahapatra, G.S., 2023. "Global stability and analysing the sensitivity of parameters of a multiple-susceptible population model of SARS-CoV-2 emphasising vaccination drive," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 741-766.
    3. Acosta-González, Eduardo & Andrada-Félix, Julián & Fernández-Rodríguez, Fernando, 2022. "On the evolution of the COVID-19 epidemiological parameters using only the series of deceased. A study of the Spanish outbreak using Genetic Algorithms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 91-104.
    4. Ahmed, Marzia & Sulaiman, Mohd Herwan & Mohamad, Ahmad Johari & Rahman, Mostafijur, 2024. "Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 248-265.

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