Relations between demographic, geographic, and environmental statistics, and the spread of novel coronavirus disease (COVID-19) in Italy

Rovetta, A.; Castaldo, L.

Background: From January 2020, the COVID-19 pandemic has raged around the world, causing nearly a million deaths and hundreds of severe economic crises. In this terrible scenario, Italy was one of the most affected countries. ------------ Objective: The aim of this study is to look for significant correlations between COVID-19 cases and demographic, geographical, and environmental statistics of each Italian region from February 26 to August 12, 2020. Finally, we further investigated the link between SARS-CoV-2 spread and particulate matter 2.5 and 10 concentrations before the lockdown in Lombardy. ------------ Methods: All demographic data were taken from the AdminStat Italia website, while the geographic data from the Il Meteo website. The collection frequency was 1 week. Data on PM2.5 and PM10 average daily concentrations were collected from previously published articles. We used Pearson's coefficients to correlate quantities that followed a normal distribution, and Spearman's coefficient to correlate quantities that did not follow a normal distribution. To evaluate this, we used the kurtosis (k) and skewness (s) coefficients according to the following scheme: we considered data compatible with a normal distribution only when t_k=k(24/n)^(-1/2) [≤] 1.5 and t_s=s(6/n)^(-1/2) [≤] 1.5; here, the Pearson correlation index was deemed more reliable. When t_k in ]1.5,3],t_s [≤] 3 or t_k [≤] 3, t_s in ]1.5,3], we considered it appropriate to evaluate both correlations. Finally, when t_k,t_s > 3, we judged the Spearman correlation index more appropriate. When the linear correlations were significant, we interpolated the data linearly. We reported in round brackets () the week in which the correlation approached the threshold of statistical significance e.g. Abruzzo (4). The chosen p-value threshold was =.05. ------------ Results: We found significant strong correlations between COVID-19 cases and population density in 60.0% of regions, such as Calabria (5), Campania (1), Lazio (1), Liguria (2), Lombardy (4), Piedmont (2), Sardinia (3), Sicily (1), and Veneto (4) (R_best=.935, 95% CI: .830 -1.000,p_best=.046,95% CI:.006-.040). The average of the angular coefficients resulting from the linear interpolations of the pairs (COVID-19 cases, population number) is b=.0037 (95% CI .0009-.0065). We found a significant strong correlation between the angular coefficients b of the various regions and their latitude. This data shows the dependence of COVID-19 on geographical and/or climatic factors (R=.926,p=.001,r=.886,p=.003). in particular, we found a significant correlation with the historical averages (last 30 years) of the minimum temperatures of the Italian regions (R=-.849,p=.008,r=-.940,p=.005 for March, R=-.923,p=.001,r=-.872,p = .005 for February). We found a significant strong correlation between the number of COVID-19 cases until August 12 and the average daily concentrations of PM2.5 in Lombardy until February 29, 2020 (r=.76,p=.004). No significant correlation with PM10 was found in the same periods. Until February 26, 2020, we found both a correlation with PM2.5 (r=.63,p=.029) and PM10 (r=.72,p=.009). In the second week of March, the correlation with PM10 disappeared while that with PM2.5 continued to exist until nowadays. We found that 40 g/m^3 for PM2.5 and 50 g/m^3 for PM10 are plausible thresholds beyond which particulate pollution clearly favors the spread of SARS-CoV-2. ------------ Conclusion: Since SARS-CoV-2 is correlated with historical minimum temperatures and particulate matter 10 and 2.5, health authorities are urged to monitor pollution levels and to invest in precautions for the arrival of autumn. Furthermore, we suggest creating awareness campaigns for the recirculation of air in closed places and to avoid exposure to cold.