Satellite data and machine learning reveal a significant correlation between NO2 and COVID-19 mortality

Abstract

The Coronavirus disease 2019 (COVID-19) pandemic has officially spread all over the world since the beginning of 2020. Although huge efforts are addressed by scientists to shed light over the several questions raised by the novel SARS-CoV-2 virus, many aspects need to be clarified, yet. In particular, several studies have pointed out significant variations between countries in per-capita mortality. In this work, we investigated the association between COVID-19 mortality with climate variables and air pollution throughout European countries using the satellite remote sensing images provided by the Sentinel-5p mission. We analyzed data collected for two years of observations and extracted the concentrations of several pollutants; we used these measurements to feed a Random Forest regression. We performed a cross-validation analysis to assess the robustness of the model and compared several regression strategies. Our findings reveal a significant statistical association between air pollution (NO₂) and COVID-19 mortality and a significant role played by the socio-demographic features, like the number of nurses or the hospital beds and the gross domestic product per capita.

Keywords: Covid-19; Pollution; Remote sensing; Sentinel-5p.

Fig. 1

Schematic flowchart of the proposed procedure. Three different data sources are explored. These data are processed to obtain a spatial map of administrative level-1 regions, the case of an Italian region (Lombardy) is shown. Finally, a regression model evaluates the association between COVID-19 mortality and pollutants.

Fig. 2

Absolute humidity over Europe; January 1 – December 2019.

Fig. 3

Correlation matrices for socio-demographic, climate and pollution features and COVID-19 mortality.

Fig. 4

RF average predictions over 5000 iterations against COVID-19 mortality (deaths per million).

Fig. 5

Comparison of the different regression models for the COVID-19 mortality prediction in terms of Pearson's correlation (left panel) and Mean Absolute Logarithmic Error (right panel).

Fig. 6

RF predictions (y-axis) against the MLP (left), SVM (top right) and LM (bottom right) predictions (x-axes).

Fig. 7

Feature Importance measured by the Boruta analysis.

Fig. 8

Comparison of the different regression models for the COVID-19 mortality prediction in terms of Pearson's correlation (left panel) and Mean Absolute Logarithmic Error (right panel).