Student Investigator: Chengkai (Tony) Tian (WSE – Applied Math & Stats)
Mentor: Jian Ni (Carey) 

The outbreak of COVID-19 alerts us to realize the importance of systematically understanding and optimizing the allocation of healthcare resources. This study integrates the sales and price data of typical over-the-counter medicines – in this study Tylenol, Aleve, and Advil – and other medical equipment such as gloves, wipes, tissue, and even food, in several cities across geographic locations, with the COVID case and death count data from Johns Hopkins COVID-panel. We supplement these data with other information such as temperature, rainfall, city demographics, age group, gender composition, income composition, and also local online search index for related symptoms. We construct a machine learning framework to investigate and identify the correlation between price floats against case counts. The study also studies how the pattern changes before and after special events such as local stock-out, mask-mandate, or stay-home order. Finally, based on the results from the training set, a potential re-optimized solution of resource allocation is proposed.