Predicting COVID-19 Resistance Using JH-CROWN Dataset

Team: Team Mountain Goats

• Program: Biomedical Engineering
• Course: Precision Care Medicine

Project Poster

Open full size poster in new tab (PDF)

Project Post Summary:

COVID-19 is a highly transmissible infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with 506 million cases worldwide and has resulted in 6.2 million deaths. Little is known about the human genetic and immunological basis of resistance to SARS-CoV-2. It has been observed that mean secondary attack rates for SARS-CoV-2 infections can reach up to 70% in some households, and several families reported that all their members except one of the spouses were infected. This suggests that some highly exposed individuals may be resistant to infection. In addition, little is known about whether the occurrence of COVID-19 resistance differs between people by health characteristics as noted in the electronic health record. In this study, we developed a machine learning model to predict COVID-19 resistance individuals with prior COVID-19 exposure using EHR data from the JH-CROWN dataset. Exploration of the dataset through clustering with Maximal-frequent All-confident pattern Selection and Pattern-based Clustering (MASPC) presented a discrepancy between patterns of diagnostic codes in resistant and non-resistant patient cohorts and XGBoost was found to have the highest performance in our modeling. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.