Analysis and Prediction of Human Mobility in the United States during the Early Stages of the COVID-19 Pandemic using Regularized Linear Models

Abstract

Since the United States started grappling with the COVID-19 pandemic, with the highest number of confirmed cases and deaths in the world as of August 2020, most states have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the long-term implications of this crisis to mobility still remain uncertain. To this end, this study proposes an analytical framework that determines the most significant factors affecting human mobility in the United States during the early days of the pandemic. Particularly, the study uses least absolute shrinkage and selection operator (LASSO) regularization to identify the most significant variables influencing human mobility and uses linear regularization algorithms, including ridge, LASSO, and elastic net modeling techniques, to predict human mobility. State-level data were obtained from various sources from January 1, 2020 to June 13, 2020. The entire data set was divided into a training and a test data set, and the variables selected by LASSO were used to train models by the linear regularization algorithms, using the training data set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that several factors, including the number of new cases, social distancing, stay-at-home orders, domestic travel restrictions, mask-wearing policy, socioeconomic status, unemployment rate, transit mode share, percent of population working from home, and percent of older (60+ years) and African and Hispanic American populations, among others, significantly influence daily trips. Moreover, among all models, ridge regression provides the most superior performance with the least error, whereas both LASSO and elastic net performed better than the ordinary linear model.