a Statistical Learning Regression Model Utilized to Determine Predictive Factors of Social Distancing During COVID-19 Pandemic

Timothy Smith, Albert Boquet, and Matthew Chin, Timothy Smith, Department of Mathematics, Embry-Riddle Aeronautical University, 1 Aerospace Boulevard, Daytona Beach, Florida 32114

In an application of the mathematical theory of statistics, predictive regression modeling can be used to determine if there is a trend to predict the response variable of social distancing in terms of multiple "predictor" input variables. In this study, the social distancing was measured as the percentage reduction in average mobility by GPS records, and the mathematical results obtained are interpreted to determine what factors drive that response. This study was done with county level data obtained from the State of Florida during the COVID-19 pandemic. The predicting factors found that were most deterministic was the county population density along with median income.



Additional Abstract Information

Presenter: Matthew Chin

Institution: Embry - Riddle Aeronautical University

Type: Poster

Subject: Mathematics

Status: Approved

Time and Location

Session: Poster 8
Date/Time: Tue 5:00pm-6:00pm
Session Number: 5577