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.
Franchi, C., Gaudio, P., Malizia, A., Albano, A., Suriano, G., Palma, M., et al. (2024). Biophysical risk in 189 Italian prisons: an inference analysis from the second to the fourth wave of the COVID-19 pandemic. THE EUROPEAN PHYSICAL JOURNAL PLUS, 139(10) [10.1140/epjp/s13360-024-05646-6].
Biophysical risk in 189 Italian prisons: an inference analysis from the second to the fourth wave of the COVID-19 pandemic
Franchi C.;Gaudio P.;Malizia A.;Di Giovanni D.
2024-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
