https://doi.org/10.1140/epjp/s13360-024-05646-6
Regular Article
Biophysical risk in 189 Italian prisons: an inference analysis from the second to the fourth wave of the COVID-19 pandemic
1
Department of Industrial Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
2
Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133, Rome, Italy
3
Office of the National Guarantor for the Rights of Persons Deprived of Personal Liberty, 00165, Rome, Italy
4
Former President of the National Guarantor for the Rights of Persons Deprived of Personal Liberty, 00165, Rome, Italy
5
School of Medicine and Surgery, Unicamillus-Saint Camillus International University of Health Sciences, 00131, Rome, Italy
a
cristiano.franchi@alumni.uniroma2.eu
Received:
29
March
2024
Accepted:
11
September
2024
Published online:
9
October
2024
Previous studies have highlighted how SARS-CoV-2 has entered Italian prisons, causing a slightly higher number of infections compared to the Italian population. This result has not been confirmed, on the contrary, with regard to the values of "Reproductive number over time" (R(t)) and "Prevalence of hospitalisation" (H). In particular, R(t), i.e. the average number of secondary transmissions caused by an infected person, is a useful parameter for measuring the infectivity of bioagents and the effectiveness of public health measures to control the spread of the infectious disease. Previous results have been obtained from data collected from all Italian prisons and therefore represent averages of many and varied realities. On the contrary, the aim of this study is to analyse and compare the Infected (I) and the R(t), in each prison, and to search for the side variables that may favour the spread of the virus in these places. The R(t) evaluation is obtained using a differential equation based on the SIR model, but unlike its usual application, no simplification has been made (i.e. population (N) ≠ susceptible (S)). Preliminary results focus on the period between September 2020 and March 2022. They show that during the second and third waves, the infection rate followed a gradient from north to south and to the islands, while the maximum R(t) values were randomly distributed across the peninsula. Moreover, we describe as the maximum R(t) values are poorly correlated with the crowding rate of each prison and the same was obtained when examining other variables such as the type of prison and the presence/absence of health isolation sections. On the contrary, there is a significant correlation when the independent variable is the presence of staff. This analysis will provide the basis for the development of other linear and nonlinear physical models that could be helpful in predicting the spread of infectious diseases in closed and controlled places.
© The Author(s) 2024
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
