Modeling the Epidemiological Trend and Behavior of COVID-19 in Italy

Abstract

As of May 14, 2020, Italy has been one of the red hotspots for the COVID-19 pandemic. In particular, the regions of Emilia Romagna, Piedmont, and especially Lombardy were the most affected and had to face very serious health emergencies, which brought them to the brink of collapse. Since the virus has demonstrated local properties, i.e., greater severity and contagiousness in specific regions, the aim of this study is to model the complex behavior of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Italy. In particular, we further investigated the results of other articles on the correlation with particulate matter pollution 10 (PM 10) and 2.5 (PM 2.5) by extending the research at the intra-regional level, as well as calculated a more plausible number of those infected compared to those officially declared by Civil Protection. Through a computational simulation of the Susceptible-Exposed-Infectious-Recovered (S.E.I.R.) model, we also estimated the most representative basic reproduction number \begin{document}R0\end{document} for these three regions from February 22 to March 14, 2020. In doing so, we have been able to evaluate the consistency of the first containment measures until the end of April, as well as identify possible SARS-CoV-2 local behavior mutations and specificities.

Relationships between Demographic, Geographic, and Environmental Statistics and the Spread of Novel Coronavirus Disease (COVID-19) in Italy

“…These could be the thresholds beyond which particulate matter significantly favors the spread of SARS-CoV-2. In addition to empirical data and other studies, this hypothesis is consistent with the first outbreak in Lodi, where the PM10 average daily concentration was the highest of the month (67 μg/m^3) [7,27]. However, our results show a greater incisiveness of PM2.5 compared to PM10 in the spread of the virus in Lombardy (Figure 1).…”

“…Finally, considering that in Lombardy, the correlation between COVID-19 cases and the number of inhabitants per region became significant after four weeks, the severity of symptoms was more severe than in other regions, and the basic reproduction number (R0) seems to have been the highest, we suggest that an evolutionary genetic mutation may have occurred in Lombardy [7]. In fact, although the genome does not appear to have changed substantially, Zhang et al showed that even small mutations could cause significant changes in SARS-CoV-2 behavior [28].…”

“…The week in which the correlation approached the threshold of statistical significance is reported in parentheses, for example, Abruzzo (4). Finally, we have deepened the results of a previous study on the search for correlations between the daily average concentrations of particulate matter (PM) 10 and 2.5 until February 29 and COVID-19 cases for every province in the Lombardy region from February 26 to August 12, 2020 [7]. The temporal discrepancy between the two detection periods is due both to the fact that COVID-19 can take up to 14 days to manifest symptoms and to our intention to test for long-term effects of PM [2,7].…”

“…To the best of our knowledge, this is the first study to provide a complete and detailed history of the correlations between the spread of SARS-CoV-2 and demographic, geographic, and environmental statistics in Italy. Since SARS-CoV-2 has shown very different virological and epidemiological characteristics 1, 2 3, 4 depending on the region [7], from the analysis of our results it was possible to highlight and mathematically quantify anomalous and/or local properties and behaviors as well as test the statistical significance of the hypotheses and scenarios proposed by other researches.…”

“…We collected data on Italian demographic statistics and pollution from the AdminStat Italia website and a previously published article [7,8]. We looked for significant Pearson (R) and Spearman (r) correlations between COVID-19 cases per province in each region from February 26 until August 12, 2020, and birth rate, median age, population density, death rate, old age index, population number, percentage of unmarried people, family members, growth rate, percentage of divorcees, percentage of foreigners, foreign growth rate, and percentage of widowers.…”