Early-phase pandemic in Italy: Covid-19 spread determinant factors

Abstract

Although the Covid-19 pandemic is still ongoing, the environmental factors beyond virus transmission are only partially known. This statistical study has the aim to identify the key factors that have affected the virus spread during the early phase of pandemic in Italy, among a wide set of potential determinants concerning demographics, environmental pollution and climate. Because of its heterogeneity in pollution levels and climate conditions, Italy provides an ideal scenario for an ecological study. Moreover, the selected period excludes important confounding factors, as different virus variants, restriction policies or vaccines. The short-term relationship between the infection maximum increase and demographic, pollution and meteo-climatic parameters was investigated, including both winter-spring and summer 2020 data, also focusing separately on the two seasonal periods and on North vs Centre-South. Among main results, the importance of population size confirmed social distancing as a key management option. The pollution hazardous role undoubtedly emerged, as NO₂ affected infection increase in all the studied scenarios, PM_2.5 manifested its impact in North of Italy, while O₃ always showed a protective action. Whereas higher temperatures were beneficial, especially in the cold season with also wind and relative humidity, solar irradiance was always relevant, revealing several significant interactions with other co-factors. Presented findings address the importance of the environment in Sars-CoV-2 spread and indicated that special carefulness should be taken in crowded areas, especially if they are highly polluted and weakly exposed to sun. The results suggest that containment of future epidemics similar to Covid-19 could be supported by reducing environmental pollution, achieving safer social habits and promoting preventive health care for better immune system response, as an only comprehensive strategy.

Keywords: Demographics; Irradiance; NO2; O3; PM2.5; Sars-CoV-2.

Fig. 1

Selected capital provinces on the Italian territory (created by internet application Google Maps, Alphabet Inc. - Mountain View, CA).

Fig. 2

Map clustering of the study outcome (3d-MaxΔ+)[n] for the selected capital provinces during (a) the 1st wave and (b) the summer. The maps were generated with Archicad v.18.

Fig. 3

Map clustering of (a) the inhabitant number [thou], (b) NO2 [μg/m³], (c) solar irradiance [MJ/m²] for the selected capital provinces during the 1st wave. The map was generated with Archicad v.18 (https://graphisoft.com/).

Fig. 4

(a-b-c-d) Simple slope graphs for significant interaction terms between irradiance and co-pollutants vs the outcome (ln 3d-MaxΔ+ (z)): (a) during 1st wave with NO₂, (b) in summer with O₃, in North of Italy (c) with NO₂ and (d) with O₃. All variables are standardized.

Fig. 5

(a-b-c) Simple slope graphs for significant interactions among the outcome (ln 3d-MaxΔ+ (z)) and meteo-climatic parameters: (a) IR with T in the total sample (1st wave + Summer), during 1st wave (b) IR with T and (c) T with rH. All variables are standardized.