Dylan Shih recently published a research paper where he used CDC data visualized with Python Seaborn and PANDAS libraries to provide an analysis of COVID-19 infection and death rate correlations to racial makeup and median state income level. The report aims to provide different stakeholders in the health sector, especially scientists, medical professionals, public health leaders, and public policymakers, an in-depth look into how the COVID-19 pandemic affects people across the nation.
The COVID-19 pandemic has undoubtedly disrupted the world, causing havoc in different countries across the globe. The United States remains one of the worst-hit countries in the world. According to a recent report, there are more than 45.4 million confirmed cases of Coronavirus worldwide, with over 1.1 deaths already recorded. Recent reports from the CDC puts the figures of confirmed COVID-19 cases in the United States at over 9.3 million with more than 235 thousand deaths recorded. Many of the figures coming out from the Center for Disease Control (CDC) and other sources provide critical basic statistics on infections but do not provide complex analysis of the context of infection or death rate compared to more than one other factor. However, Dylan Shih is looking to offer more information in his newly published report, which can serve to help policy makers and health care professionals make more informed decisions.
The paper investigates the infection/death rates in terms of race, socioeconomic factors, and state income levels. The study seeks to look for any possible correlation between income and other socioeconomic factors affecting COVID-19 on a statewide level. The researcher looks at the relatively higher rate of infection in African American, Hispanic, and Native American populations as compared to Caucasians and Asians. The study concludes that although race is correlated to infection and death rate, average state income level is not. Thus, further research into socio-economic correlations of Covid is required in order to guide policy decisions on healthcare funding, lockdown protocols, vaccine distribution, etc.
For more information about the report, please visit – www.SarsCovid19Research.com.
SarsCovid19Research is an online platform designed to provide a visualization of various statistical data sets related to Covid-19 in the United States. The team at SarsCovid19Research is led by Dylan Shih, a student of data science and statistics with experience in conducting independent research on public health topics such as Covid-19. He has interned at SSRC at Cal State Fullerton.