Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland

Abstract

A growing number of various studies focusing on different aspects of the COVID-19 pandemic are emerging as the pandemic continues. Three variables that are most commonly used to describe the course of the COVID-19 pandemic worldwide are the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered. In this paper, using the multiscale geographically weighted regression, an analysis of the interrelationships between the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered were conducted. Furthermore, using maps of the local R2 estimates, it was possible to visualize how the relations between the explanatory variables and the dependent variables vary across the study area. Thus, analysis of the influence of demographic factors described by the age structure and gender breakdown of the population over the course of the COVID-19 pandemic was performed. This allowed the identification of local anomalies in the course of the COVID-19 pandemic. Analyses were carried out for the area of Poland. The results obtained may be useful for local authorities in developing strategies to further counter the pandemic.