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

Massimo Bilancia; Domenico Vitale; Fabio Manca; Paola Perchinunno; Luigi Santacroce

doi:10.1007/s10182-023-00469-9

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

Adv Stat Anal. 2023 Feb 7:1-30. doi: 10.1007/s10182-023-00469-9. Online ahead of print.

Authors

Massimo Bilancia¹, Domenico Vitale², Fabio Manca³, Paola Perchinunno⁴, Luigi Santacroce⁵

Affiliations

¹ Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Policlinic University Hospital - Piazza G. Cesare 11, 70124 Bari, Italy.
² MEMOTEF Department, University of Roma La Sapienza, Via del Castro Laurenziano 9, 00161 Rome, Italy.
³ Department of Education, Psychology, Communication (ForPsiCom), University of Bari Aldo Moro, Palazzo Chiaia Napolitano - Via S. Crisanzio 42, 70122 Bari, Italy.
⁴ Department of Business and Law Studies (DEMDI), University of Bari Aldo Moro, Largo Abbazia di Santa Scolastica 53, 70124 Bari, Italy.
⁵ Department of Interdisciplinary Medicine (DIM) and Microbiology and Virology Unit, University of Bari Aldo Moro, Policlinic University Hospital - Piazza G. Cesare 11, 70124 Bari, Italy.

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: Bayesian computations; Bayesian modeling; COVID-19; Causal analysis; Gaussian processes; State-space models.