Covid Vaccine Risk Stratification

In response to the pandemic caused by the rapidly spreading COVID-19 virus, several highly effective vaccines have been developed by Pfizer, Moderna, and Janssen. Despite the promising efficacy of those vaccines, there remains the challenge of properly distributing vaccines to those who need it most in the US. Of particular concern are individuals who are at higher risk due to underlying medical conditions which have been shown to exacerbate COVID-19 symptoms and at times lead to fatal illnesses. In addition to this, a variety of socioeconomic factors have been linked to increased COVID-19 rates and increased mortality, such as race, age, income, mobility, and education level.This project aims to develop an information system to help advise vaccine distributors and state governments on how to effectively distribute vaccines to prioritize high risk individuals. The information system incorporates state-level data of the population with underlying medical conditions, demographics, overall state income, education level, and state mobility to formulate a mortality index. State-level data on the number of vaccines available and doses already administered are also incorporated into the information system to generate a vaccine index. The mortality and vaccine indices for each state are coupled to generate a vaccine priority ranking which can be used to advise vaccine distribution.The prototype can successfully link the data described above to a map of the US and then color code states according to the vaccine priority ranking. Implementation of this prototype will enable optimal vaccine distribution and reduce instances of severe or fatal COVID-19 illnesses as well as reduce costs associated with oversupply of vaccines in a single region. Future work will focus on improving the granularity of data down to the county-level, as well as increasing the scope of the system to the global scale. Additionally, the team plans to expand the application space of this information system to other diseases.