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Estimating the Risk of Contracting COVID-19 in Different Settings Using a Multiscale Transmission Dynamics Model

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  • Dramane Sam Idris Kanté

    (LAMAI, Faculty of Sciences and Technics, Department of Mathematics, Cadi Ayyad University, Marrakesh 40140, Morocco
    Centrale Casablanca, Complex Systems and Interactions Research Center, Ville Verte, Bouskoura 27182, Morocco)

  • Aissam Jebrane

    (Centrale Casablanca, Complex Systems and Interactions Research Center, Ville Verte, Bouskoura 27182, Morocco)

  • Anass Bouchnita

    (Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA)

  • Abdelilah Hakim

    (LAMAI, Faculty of Sciences and Technics, Department of Mathematics, Cadi Ayyad University, Marrakesh 40140, Morocco)

Abstract

Airborne transmission is the dominant route of coronavirus disease 2019 (COVID-19) transmission. The chances of contracting COVID-19 in a particular situation depend on the local demographic features, the type of inter-individual interactions, and the compliance with mitigation measures. In this work, we develop a multiscale framework to estimate the individual risk of infection with COVID-19 in different activity areas. The framework is parameterized to describe the motion characteristics of pedestrians in workplaces, schools, shopping centers and other public areas, which makes it suitable to study the risk of infection under specific scenarios. First, we show that exposure to individuals with peak viral loads increases the chances of infection by 99%. Our simulations suggest that the risk of contracting COVID-19 is especially high in workplaces and residential areas. Next, we determine the age groups that are most susceptible to infection in each location. Then, we show that if 50% of the population wears face masks, this will reduce the chances of infection by 8%, 32%, or 45%, depending on the type of the used mask. Finally, our simulations suggest that compliance with social distancing reduces the risk of infection by 19%. Our framework provides a tool that assesses the location-specific risk of infection and helps determine the most effective behavioral measures that protect vulnerable individuals.

Suggested Citation

  • Dramane Sam Idris Kanté & Aissam Jebrane & Anass Bouchnita & Abdelilah Hakim, 2023. "Estimating the Risk of Contracting COVID-19 in Different Settings Using a Multiscale Transmission Dynamics Model," Mathematics, MDPI, vol. 11(1), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:254-:d:1024212
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    References listed on IDEAS

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    1. Xiao, Yao & Yang, Mofeng & Zhu, Zheng & Yang, Hai & Zhang, Lei & Ghader, Sepehr, 2021. "Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach," Transport Policy, Elsevier, vol. 109(C), pages 12-23.
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    3. Dennis M. Feehan & Ayesha S. Mahmud, 2021. "Quantifying population contact patterns in the United States during the COVID-19 pandemic," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    4. Bouchnita, Anass & Jebrane, Aissam, 2020. "A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    5. Bosina, Ernst & Weidmann, Ulrich, 2017. "Estimating pedestrian speed using aggregated literature data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 1-29.
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    Cited by:

    1. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Tierry Mitonsou Hounkonnou & Laure Gouba, 2024. "Differential Equations and Applications to COVID-19," Mathematics, MDPI, vol. 12(17), pages 1-15, September.

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