Building a COVID-19 Vulnerability Index

Dave DeCaprio; Joseph A Gartner; Thadeus Burgess; Sarthak Kothari; Shaayaan Sayed; Carol J McCall

We highlight 3 approaches to building models to help identify individuals who are vulnerable to complications to respiratory infections. All 3 approaches described are machine learning methods created using the same dataset. We have chosen 3 different approaches that represent a tradeoff between accuracy and ease of implementation. For individuals who have access to data, but not the coding background to adopt our model, we hope that the simple model can be easily ported to other systems. For a more robust model, we create a gradient-boosted tree leveraging age, sex, and medical diagnosis history. This model has been made open-source, and can be obtained from github (https://github.com/closedloop-ai/cv19index). Finally, we have created a third model that uses an extensive feature set generated from Medicare claims data along with linked geographical and social determinants of health data. This model is being made freely available through our hosted platform. Information about accessing the platform can be found at https://cv19index.com.