Biomedical Statistics and Informatics 2021; 6(1): 1-5 http://www.sciencepublishinggroup.com/j/bsi doi: 10.11648/j.bsi.20210601.11 ISSN: 2578-871X (Print); ISSN: 2578-8728 (Online) Review Article Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries Uri Eliyahu 1, * , Avi Magid 2 1 Department of Nutrition, Faculty of Health Sciences, Ariel University, Ariel, Israel 2 Department of Health System Management, Peres Academic Center, Rehovot, Israel Email address: * Corresponding author To cite this article: Uri Eliyahu, Avi Magid. Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries. Biomedical Statistics and Informatics. Vol. 6, No. 1, 2021, pp. 1-5. doi: 10.11648/j.bsi.20210601.11 Received: December 30, 2020; Accepted: January 9, 2021; Published: January 22, 2021 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 (R 2 =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. Keywords: Pandemic, Linear Model, Demographics, Mortality, European 1. Introduction By April 5, 2020, the outbreak of the coronavirus disease 2019 (COVID-19) caused 1,318,713 confirmed cases and 73,146 deaths globally. These numbers are much higher than those of the 2003 Severe Acute Respiratory Syndrome (SARS) (8273 cases, 775 deaths,) and the 2012 Middle East Respiratory Syndrome (MERS) (1139 cases, 431 deaths). Since its outbreak, COVID-19 was detected four months later internationally [1]. Although SARS and MERS are considered much more fatal compared to COVID-19, the latter tends to spread at a higher rate and infect considerably more people [2]. Among designated groups (males, over 75 years of age with background disease), the fatality rate of this disease could rise to 14.2% and above [3]. Due to the COVID-19 high mortality rate in certain populations predisposed to death, it is important to develop a model that could predict its influence based on demographical and minimal clinical data. Over the years, many models have been developed to predict mortality from infectious diseases. These models are based on the classical epidemiological approach Known as the acronym SEIR models: S for