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Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models

Author

Listed:
  • Ron S. Kenett

    (KPA Group and Samuel Neaman Institute, Raanana 43100, Israel)

  • Giancarlo Manzi

    (Data Science Research Centre, Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy)

  • Carmit Rapaport

    (Department of Geography and Environmental Studies, University of Haifa, Haifa 3498838, Israel
    NIRED—National Institute for Regulation of Emergency and Disaster, College of Law and Business, Ramat Gan 5110801, Israel)

  • Silvia Salini

    (Data Science Research Centre, Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy)

Abstract

The response to the COVID-19 pandemic has been highly variable. Governments have applied different mitigation policies with varying effect on social and economic measures, over time. This article presents a methodology for examining the effect of mobility restriction measures and the association between health and population activity data. As case studies, we refer to the pre-vaccination experience in Italy and Israel. Facing the pandemic, Israel and Italy implemented different policy measures and experienced different population behavioral patterns. Data from these countries are used to demonstrate the proposed methodology. The analysis we introduce in this paper is a staged approach using Bayesian Networks and Structural Equations Models. The goal is to assess the impact of pandemic management and mitigation policies on pandemic spread and population activity. The proposed methodology models data from health registries and Google mobility data and then shows how decision makers can conduct scenario analyses to help design adequate pandemic management policies.

Suggested Citation

  • Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4859-:d:795539
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    References listed on IDEAS

    as
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    2. Bargain, Olivier & Aminjonov, Ulugbek, 2020. "Trust and compliance to public health policies in times of COVID-19," Journal of Public Economics, Elsevier, vol. 192(C).
    3. Ron S. Kenett & Silvia Salini, 2011. "Rejoinder to ‘Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 484-486, September.
    4. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    5. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
    6. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.
    7. Gupta, Sumeet & Kim, Hee W., 2008. "Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities," European Journal of Operational Research, Elsevier, vol. 190(3), pages 818-833, November.
    8. Painter, Marcus & Qiu, Tian, 2021. "Political beliefs affect compliance with government mandates," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 688-701.
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    Cited by:

    1. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.

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