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

Ron S. Kenett, Giancarlo Manzi, Carmit Rapaport, Silvia Salini

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number4859
JournalInternational Journal of Environmental Research and Public Health
Issue number8
StatePublished - 16 Apr 2022

Bibliographical note

Funding Information:
Funding: Funding for this research was provided by the APC project—University of Milan, Italy.

Funding Information:
Acknowledgments: The authors acknowledge support from the University of Milan through the APC initiative.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • Bayesian Networks
  • COVID-19 pandemic
  • integrated models
  • SEM

ASJC Scopus subject areas

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis


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