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Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach

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  • Mahapatra, D.P.
  • Triambak, S.

Abstract

Phenomenological and deterministic models are often used for the estimation of transmission parameters in an epidemic and for the prediction of its growth trajectory. Such analyses are usually based on single peak outbreak dynamics. In light of the present COVID-19 pandemic, there is a pressing need to better understand observed epidemic growth with multiple peak structures, preferably using first-principles methods. Along the lines of our previous work [Physica A 574, 126014 (2021)], here we apply 2D random-walk Monte Carlo calculations to better understand COVID-19 spread through contact interactions. Lockdown scenarios and all other control interventions are imposed through mobility restrictions and a regulation of the infection rate within the stochastically interacting population. The susceptible, infected and recovered populations are tracked over time, with daily infection rates obtained without recourse to the solution of differential equations.

Suggested Citation

  • Mahapatra, D.P. & Triambak, S., 2022. "Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:chsofr:v:156:y:2022:i:c:s0960077921011383
    DOI: 10.1016/j.chaos.2021.111785
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    References listed on IDEAS

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    1. Ewen Callaway & Heidi Ledford, 2021. "How bad is Omicron? What scientists know so far," Nature, Nature, vol. 600(7888), pages 197-199, December.
    2. Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    3. Triambak, S. & Mahapatra, D.P., 2021. "A random walk Monte Carlo simulation study of COVID-19-like infection spread," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    4. Alexander Karaivanov, 2020. "A social network model of COVID-19," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-33, October.
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    Cited by:

    1. Pájaro, Manuel & Fajar, Noelia M. & Alonso, Antonio A. & Otero-Muras, Irene, 2022. "Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

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