USING QUANTIFIABLE BEHAVIORAL TRAITS TO PREDICT A COUNTRY’S COVID19 INFECTION RATES

by Charles Alba, Manasvi Mittal and Anmolika Singh

COVID19 has shown that indicators that are a function of a nation's economy and healthcare infrastructure are inaccurate in predicting a country's outcomes should a health pandemic occur. Our poster suggests the utilization of quantifiable traits like Individualism, Power Distance, Masculinity, Uncertainty avoidance, long-term orientation, and Indulgence to predict a country's COVID19 infection rates. This is accomplished by applying machine learning techniques like multi-variate imputation and Poisson regression against COVID and behavioral datasets.

Major: 
Data Science
Exhibition Category: 
Social and Behavioral Sciences
Exhibition Format: 
Poster Presentation
Campus: 
University Park
Faculty Sponsor: 
James Wang
Poster Number: 
16095