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A random walk Monte Carlo simulation study of COVID-19-like infection spread

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  • Triambak, S.
  • Mahapatra, D.P.

Abstract

Recent analysis of early COVID-19 data from China showed that the number of confirmed cases followed a subexponential power-law increase, with a growth exponent of around 2.2 (Maier and Brockmann, 2020). The power-law behavior was attributed to a combination of effective containment and mitigation measures employed as well as behavioral changes by the population. In this work, we report a random walk Monte Carlo simulation study of proximity-based infection spread. Control interventions such as lockdown measures and mobility restrictions are incorporated in the simulations through a single parameter, the size of each step in the random walk process. The step size l is taken to be a multiple of 〈r〉, which is the average separation between individuals. Three temporal growth regimes (quadratic, intermediate power-law and exponential) are shown to emerge naturally from our simulations. For l=〈r〉, we get intermediate power-law growth exponents that are in general agreement with available data from China. On the other hand, we obtain a quadratic growth for smaller step sizes l≲〈r〉∕2, while for large l the growth is found to be exponential. We further performed a comparative case study of early fatality data (under varying levels of lockdown conditions) from three other countries, India, Brazil and South Africa. We show that reasonable agreement with these data can be obtained by incorporating small-world-like connections in our simulations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:574:y:2021:i:c:s0378437121002867
    DOI: 10.1016/j.physa.2021.126014
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    References listed on IDEAS

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    1. Filipe, J.A.N., 1999. "Hybrid closure-approximation to epidemic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 266(1), pages 238-241.
    2. Frank Schlosser & Benjamin F. Maier & Olivia Jack & David Hinrichs & Adrian Zachariae & Dirk Brockmann, 2020. "COVID-19 lockdown induces disease-mitigating structural changes in mobility networks," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(52), pages 32883-32890, December.
    3. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
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    Cited by:

    1. Gilberto Gonzalez-Parra & Abraham J. Arenas, 2021. "Nonlinear Dynamics of the Introduction of a New SARS-CoV-2 Variant with Different Infectiousness," Mathematics, MDPI, vol. 9(13), pages 1-22, July.
    2. 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).
    3. Pascoal, R. & Rocha, H., 2022. "Population density impact on COVID-19 mortality rate: A multifractal analysis using French data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    4. Panicker, Akhil & Sasidevan, V., 2024. "Social adaptive behavior and oscillatory prevalence in an epidemic model on evolving random geometric graphs," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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