Population Statistics that Contribute to COVID Mortality

Our primary objective in this paper was to determine the impact of various factorsaffecting disproportionate COVID mortality rates between counties in the United States. We primarily relied on the CDC’s demographics data and the CDC’s data on COVID andcomorbidities in US counties. We used these datasets to visualize mortality rates andco-morbidity rates. Exploratory data analysis was then performed to attempt to find trends. Afterwards, we fit our data to a linear regression model to identify the factors that contributed most to the model. The most important features of our model was the proportion of the population that was male and the median age. We found that the median age of the population was a stronger predictor of COVID mortality than presence of comorbidities like diabetes and heart disease. More analysis has yet to be done on the intersection of various comorbidities and median age.