We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June. In the first estimate, the results highlight the dominance of factors related to the intensity and interactions of economic activities. In the second, the relevance of these variables is highly reduced, suggesting mitigation of the pandemic following the lockdown of the economy. Finally, by considering cases at onset of the “second wave”, we confirm that the territorial distribution of the epidemic is associated with economic factors.

Bloise, F., & Tancioni, M. (2021). Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?. STRUCTURAL CHANGE AND ECONOMIC DYNAMICS, 56, 310-329 [10.1016/j.strueco.2021.01.001].

Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?

Bloise F.;
2021

Abstract

We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June. In the first estimate, the results highlight the dominance of factors related to the intensity and interactions of economic activities. In the second, the relevance of these variables is highly reduced, suggesting mitigation of the pandemic following the lockdown of the economy. Finally, by considering cases at onset of the “second wave”, we confirm that the territorial distribution of the epidemic is associated with economic factors.
Bloise, F., & Tancioni, M. (2021). Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?. STRUCTURAL CHANGE AND ECONOMIC DYNAMICS, 56, 310-329 [10.1016/j.strueco.2021.01.001].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/380490
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