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.
|Journal||International Journal of Environmental Research and Public Health|
|State||Published - 16 Apr 2022|
Bibliographical noteFunding Information:
Funding: Funding for this research was provided by the APC project—University of Milan, Italy.
Acknowledgments: The authors acknowledge support from the University of Milan through the APC initiative.
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Bayesian Networks
- COVID-19 pandemic
- integrated models
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis