Introduction Starting in late April, as the COVID-19 pandemic swept across the United States, several news stories and articles were published highlighting a racial bias in COVID-19 infections and deaths. These articles were followed by counterpoints suggesting that the racial discrepancy in COVID-19 infections was not surprising and could be explained by differences in socio-economic status and access to health care, which are divided along racial lines. Curious to see what the consensus was regarding the underlying cause of the discrepancy was, I looked for more resources. I struggled to find a paper or other analysis that considered a variety of demographic and socio-economic covariates when assessing the importance of race on COVID-19 infections.
Introduction The long term objective for this modelling project is to investigate the relationship between demographic variables such as race, ethnicity, gender, and others and the mortality rates due to COVID-19. The primary dataset that I will be using for this project is the number of deaths per county in the US, tabulated daily. This means that each county is a timeseries of counts of deaths due to COVID-19. Time series models and especially regressions with timeseries is a large subject area, and in this blog post we are going to dive into the basics of timeseries modelling with the added twist of working with count data.