Numerical analysis of factors, pace and intensity of the corona virus (COVID-19) epidemic in Poland

Piotr Andrzej Kowalski; Marcin Szwagrzyk; Jolanta Kielpinska; Aleksander Konior; Maciej Kusy

doi:10.1016/j.ecoinf.2021.101284

Numerical analysis of factors, pace and intensity of the corona virus (COVID-19) epidemic in Poland

Ecol Inform. 2021 Jul:63:101284. doi: 10.1016/j.ecoinf.2021.101284. Epub 2021 Mar 29.

Authors

Piotr Andrzej Kowalski^{1

2}, Marcin Szwagrzyk^{3

4}, Jolanta Kielpinska⁵, Aleksander Konior⁴, Maciej Kusy⁶

Affiliations

¹ Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland.
² Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland.
³ Faculty of Geography and Geology, Institute of Geography and Spatial Management, Jagiellonian University, Gronostajowa 7, 30-387 Cracow, Poland.
⁴ Airly Inc., ul. Mogilska 43, 31-545 Cracow, Poland.
⁵ Department of Aquatic Bioengineering and Aquaculture, Faculty of Food Science and Fisheries, West Pomeranian University of Technology in Szczecin, Poland.
⁶ Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.

Abstract

This article focuses on a statistical analysis of the corona virus disease 2019 (COVID-19) data that appeared until November 31, 2020 in Poland. The studied database, expressed in terms of both population and air pollution (particulate) indicators, is provided mainly by the Airly company, the Central Statistical Office (GUS) and the Rogalski project. The particular measured factors, which underwent standardization, were assessed for mutual dependency by means of a Pearson correlation coefficient and analysed by a linear regression. Based on the presented models, our results indicate that air quality (air pollution level) is the most important factor in the context of enabling COVID-19 case load increase in Poland.

Keywords: COVID-19; Corona virus; Disease curve; Epidemiological model; Excess mortality; Factor analysis; Least-squares estimation; Multivariate linear regression; Pearson's correlation.