A risk scorecard for COVID-19

A product of the UAB COVID-19 Data Science Hackathon

On June 15 and 16, I participated in the UAB COVID-19 Data Science Hackathon. My teammates and I created a scorecard model and web app that predicts the likelihood of an individual being infected with COVID-19 based on demographics, pre-existing conditions, and symptoms. We were one of three teams to win a cash prize in the competition for the novelty and success of our work.

My teammates were Thi K. Tran-Nguyen, Tarun Karthik Kumar Mamidi, and Liz Worthey (who proposed the question we focused on). Together, our skills covered the gamut of data wrangling, model development, and medical relevance. As a result, we developed a proof of concept model and web app that we envision could be used by a broad population including both patients and providers. The end result is like looking at your credit score, except that it tells you if you are at higher risk of having COVID-19.

Scorecard web app demo prepared by Tarun Mamidi.

This work may be of particular interest to perioperative medicine for both specific patient screening for COVID-19 and risk stratification methods in general.

The hackathon showcase presentations were recorded on Friday, June 19, 2020. To view the presentation for this visit, click here.

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