A Coronavirus Cohort Case Study - Dataset Trends using Machine Learning Methods

In this cohort study, we analyzed data collected from Drexel University students, faculty, and staff (age 18 – 79) using machine learning models to gain insight into the significance and predictive capabilities of the the features collected. Data from 126,983 SARS-CoV-2 tests was collected from 16,914 unique individuals from March 4, 2020 to April 24, 2022. Associated symptom data (551,257 reports) was collected through the Drexel University Health Checker App powered by the industry partner, Respond Health. 4,457 people had a positive SARS-CoV-2 test result within the study timeframe. 2,074 of the 4,457 (46.53%) positive cases were in individuals that were fully vaccinated. Given the comprehensive data collected over the entire period of the pandemic, we are able to explore the trends and importance of features to their predictive capabilities. In our experiments and results, we analyze the relative importance of the collected features during different time periods of the COVID-19 evolution and present the trends over time.