Predicting economic resilience of territories in Italy during the COVID-19 first lockdown.
Expert Syst Appl
; 232: 120803, 2023 Dec 01.
Article
en En
| MEDLINE
| ID: mdl-37363270
ABSTRACT
This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeconomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems.
Texto completo:
1
Banco de datos:
MEDLINE
Tipo de estudio:
Health_economic_evaluation
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Expert Syst Appl
Año:
2023
Tipo del documento:
Article
País de afiliación:
Italia