Environmental Variables and their Relation with the SARS-COV-2 Transmission: A Data Mining Approach

Abstract

In the present paper are analyzed the correlations between the meteorological variables such as maximum temperature, minimum temperature, relative humidity, precipitation with the number of infections by SARS-CoV-2. The data set for the meteorological variables was obtained from web site of the National Service of Meteorology and Hydrology of Peru (SENAMHI), while the number of infections by SARS-CoV-2 was obtained from the denominated data positive by COVID 19 reported in web site by the Ministry of Health from Peru. After the preprocessing and the fusion of the data sets, it was obtained a data subset with 365 registers and 6 columns. To detect the correlations between the meteorological variables and the number of infections by SARS-CoV-2, the Spearman’s rank correlation coefficient was computed. The results show significant correlations between the variables minimum temperature and number of infections by SARS-CoV-2 for a rho=-0.45, p-valor=0.00<0.05; relative humidity and number of infections by SARS-CoV-2 for a rho=-0.24, p-valor=0.00<0.05; precipitation and number of infections by SARS-CoV-2 for a rho=-0.24, p-valor=0.00<0.05. According to the results obtained, we concluded that minimum temperatures facilitate the SARS-CoV-2 transmissibility.