Abstract
Background: Coronavirus Disease 2019 (COVID-19) is the main discussed topic worldwide in 2020 and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. Objectives: In this paper, a data analytics study on the diffusion of COVID-19 in Lombardy Region and Campania Region is developed in order to identify the driver that sparked the second wave in Italy. Methods: Starting from all the available official data collected about the diffusion of COVID-19, we analyzed Google mobility data, school data and infection data for two big regions in Italy: Lombardy Region and Campania Region, which adopted two different approaches in opening and closing schools. To reinforce our findings, we also extended the analysis to the Emilia Romagna Region. Results: The paper shows how different policies adopted in school opening/closing may have had an impact on the COVID-19 spread, while other factors related to citizen mobility did not affect the second Italian wave. Conclusions: The paper shows that a clear correlation exists between the school contagion and the subsequent temporal overall contagion in a geographical area. Moreover, it is clear that highly populated provinces have the greatest spread of the virus.
Highlights
Data analysis [1,2,3] has proved to be of fundamental importance for studying and predicting the behavior of the pandemic of SARS-CoV2 and COVID-19, in order to intervene promptly and stem its spread [4,5,6]
In order to understand the relation between schools and global infection, we considered the data officially released by the MIUR and we carried out a correlation analysis on the Lombardy Region (RL) and Campania Region (RC), two regions that have adopted two different policies of opening and closing schools
RL reopened all primary and secondary schools in presence at 14 September 2020 [25]; RC reopened all primary and secondary schools in presence at 24 September 2020 and all levels were closed in advance starting from the October 16 and until November [26]; REm reopened all primary and secondary schools in presence at September 2020
Summary
Data analysis [1,2,3] has proved to be of fundamental importance for studying and predicting the behavior of the pandemic of SARS-CoV2 and COVID-19, in order to intervene promptly and stem its spread [4,5,6]. Effects of schools opening and the propagation of COVID-19 are described in other countries, such as in [12] where the effects of school openings on hospitalization in USA are modeled, or in [11] where the authors explain how UK schools are causing COVID-19 spreading and how to act to reduce their impact. The data (shown in Table 1) on the growth of infections by age groups from the beginning of September to March that are published weekly in the epidemiological reports of the ISS (Istituto Superiore Sanità) [www.iss.it] (accessed on 27 April 2021), indicate that the age group 0–9 have had a growth between 6 and 10 times higher than all other ages. The ratio of these cases in the younger population is probably much higher than in the elder population.)
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