A dynamic causal modeling of the second outbreak of COVID-19 in Italy

Massimo Bilancia (), Domenico Vitale (), Fabio Manca (), Paola Perchinunno () and Luigi Santacroce ()
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Massimo Bilancia: University of Bari Aldo Moro
Domenico Vitale: University of Roma La Sapienza
Fabio Manca: University of Bari Aldo Moro
Paola Perchinunno: University of Bari Aldo Moro
Luigi Santacroce: University of Bari Aldo Moro

AStA Advances in Statistical Analysis, 2024, vol. 108, issue 1, No 1, 30 pages

Abstract: Abstract While the vaccination campaign against COVID-19 is having its positive impact, we retrospectively analyze the causal impact of some decisions made by the Italian government on the second outbreak of the SARS-CoV-2 pandemic in Italy, when no vaccine was available. First, we analyze the causal impact of reopenings after the first lockdown in 2020. In addition, we also analyze the impact of reopening schools in September 2020. Our results provide an unprecedented opportunity to evaluate the causal relationship between the relaxation of restrictions and the transmission in the community of a highly contagious respiratory virus that causes severe illness in the absence of prophylactic vaccination programs. We present a purely data-analytic approach based on a Bayesian methodology and discuss possible interpretations of the results obtained and implications for policy makers.

Keywords: COVID-19; Causal analysis; Gaussian processes; State-space models; Bayesian modeling; Bayesian computations (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10182-023-00469-9

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