IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1156-d1081070.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1156/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/5/1156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. X. Angela Yao & Andrew Crooks & Bin Jiang & Jukka Krisp & Xintao Liu & Haosheng Huang, 2023. "An overview of urban analytical approaches to combating the Covid-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1133-1143, June.
    3. Pelinovsky, E. & Kokoulina, M. & Epifanova, A. & Kurkin, A. & Kurkina, O. & Tang, M. & Macau, E. & Kirillin, M., 2022. "Gompertz model in COVID-19 spreading simulation," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    4. Christian Alemán & Christopher Busch & Alexander Ludwig & Raül Santaeulà lia-Llopis, 2020. "Evaluating the Effectiveness of Policies Against a Pandemic," Working Papers 2020-078, Human Capital and Economic Opportunity Working Group.
    5. Se Yoon Lee & Bani K. Mallick, 2022. "Bayesian Hierarchical Modeling: Application Towards Production Results in the Eagle Ford Shale of South Texas," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 1-43, May.
    6. Pavlov, Konstantin & Timiryanova, Venera & Yusupov, Kasim & Krasnoselskaya, Dina, 2022. "Анализ волн распространения Covid-19 в России [Analysis of Covid-19 wave distribution in Russia]," MPRA Paper 114637, University Library of Munich, Germany.
    7. Xie, Xuemei & Liu, Xiaojie & Blanco, Cristina, 2023. "Evaluating and forecasting the niche fitness of regional innovation ecosystems: A comparative evaluation of different optimized grey models," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    8. Ming Sun & Tiange Xu, 2024. "Identification and Management of Epidemic Hazard Areas for Urban Sustainability: A Case Study of Tongzhou, China," Sustainability, MDPI, vol. 16(18), pages 1-25, September.
    9. Stef Baas & Sander Dijkstra & Aleida Braaksma & Plom Rooij & Fieke J. Snijders & Lars Tiemessen & Richard J. Boucherie, 2021. "Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units," Health Care Management Science, Springer, vol. 24(2), pages 402-419, June.
    10. I Gede Nyoman Mindra Jaya & Farah Kristiani & Yudhie Andriyana & Anna Chadidjah, 2024. "Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
    11. Demetrius E. Davos & Ioannis C. Demetriou, 2022. "Convex-Concave fitting to successively updated data and its application to covid-19 analysis," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3233-3262, December.
    12. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.
    13. Duan, Huiming & Nie, Weige, 2022. "A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    14. Daniele Lilleri & Federica Zavaglio & Elisa Gabanti & Giuseppe Gerna & Eloisa Arbustini, 2020. "Analysis of the SARS-CoV-2 epidemic in Italy: The role of local and interventional factors in the control of the epidemic," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-12, November.
    15. Dijkstra, Sander & Baas, Stef & Braaksma, Aleida & Boucherie, Richard J., 2023. "Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy," Omega, Elsevier, vol. 116(C).
    16. Ángel Berihuete & Marta Sánchez-Sánchez & Alfonso Suárez-Llorens, 2021. "A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    17. Se Yoon Lee, 2022. "Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications," Mathematics, MDPI, vol. 10(6), pages 1-51, March.
    18. Jesus Cerquides, 2021. "A First Approach to Closeness Distributions," Mathematics, MDPI, vol. 9(23), pages 1-12, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1156-:d:1081070. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.