Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries

Abstract

Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.