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Mathematical and Statistical Modelling for Assessing COVID-19 Superspreader Contagion: Analysis of Geographical Heterogeneous Impacts from Public Events

Author

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  • Conceição Leal

    (Department of Science and Technology, Universidade Aberta, 1269-001 Lisboa, Portugal
    CEAUL (Center of Statistics and Applications of the University of Lisbon), 1649-014 Lisboa, Portugal)

  • Leonel Morgado

    (Department of Science and Technology, Universidade Aberta, 1269-001 Lisboa, Portugal
    INESC TEC (Institute for Systems and Computing Engineering, Technology and Science), 4200-465 Porto, Portugal)

  • Teresa A. Oliveira

    (Department of Science and Technology, Universidade Aberta, 1269-001 Lisboa, Portugal
    CEAUL (Center of Statistics and Applications of the University of Lisbon), 1649-014 Lisboa, Portugal)

Abstract

During a pandemic, public discussion and decision-making may be required in face of limited evidence. Data-grounded analysis can support decision-makers in such contexts, contributing to inform public policies. We present an empirical analysis method based on regression modelling and hypotheses testing to assess events for the possibility of occurrence of superspreading contagion with geographically heterogeneous impacts. We demonstrate the method by evaluating the case of the May 1st, 2020 Demonstration in Lisbon, Portugal, on regional growth patterns of COVID-19 cases. The methodology enabled concluding that the counties associated with the change in the growth pattern were those where likely means of travel to the demonstration were chartered buses or private cars, rather than subway or trains. Consequently, superspreading was likely due to travelling to/from the event, not from participating in it. The method is straightforward, prescribing systematic steps. Its application to events subject to media controversy enables extracting well founded conclusions, contributing to informed public discussion and decision-making, within a short time frame of the event occurring.

Suggested Citation

  • Conceição Leal & Leonel Morgado & Teresa A. Oliveira, 2023. "Mathematical and Statistical Modelling for Assessing COVID-19 Superspreader Contagion: Analysis of Geographical Heterogeneous Impacts from Public Events," Mathematics, MDPI, vol. 11(5), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1156-:d:1081070
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    References listed on IDEAS

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    1. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    2. Luo, Xilin & Duan, Huiming & Xu, Kai, 2021. "A novel grey model based on traditional Richards model and its application in COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
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