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

Ron Kenett, Giancarlo Manzi, Carmit Rapaport and Silvia Salini
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Carmit Rapaport: Department of Geography and Environmental Studies, University of Haifa, Haifa 3498838, Israel
Silvia Salini: Data Science Research Centre, Department of Economics, Management and Quantitative Methods, University of Milan, 20122 Milan, Italy

IJERPH, 2022, vol. 19, issue 8, 1-26

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.

Keywords: Bayesian Networks; SEM; COVID-19 pandemic; integrated models (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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