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

• Published in: Advances in statistical analysis : AStA : a journal of the German Statistical Society Vol. 108; no. 1; pp. 1 - 30
• Main Authors: Bilancia, Massimo, Vitale, Domenico, Manca, Fabio, Perchinunno, Paola, Santacroce, Luigi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024; Springer Nature B.V
• Subjects: Bayesian analysis; Decision analysis; Econometrics; Economics; Finance; Impact analysis; Insurance; Management; Mathematics and Statistics; Original Paper; Outbreaks; Probability Theory and Stochastic Processes; Statistics; Statistics for Business; Viral diseases; COVID-19; Bayesian computations; Causal analysis; Bayesian modeling; State-space models; Gaussian processes
• ISSN: 1863-8171; 1863-818X
• DOI: 10.1007/s10182-023-00469-9

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.