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.

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

“…And forecasting the upcoming data can be done by averaging the results from the position nearest to the point to be searched by the number of values of K shown in (4).…”

“…The international committee on virus classification has given the official name COVID-19, and is derived from coronavirus disease 2019. The World Health Organization has declared the COVID-19 outbreak a Public Health Emergency of International Concern [2]- [4]. Coronavirus is a multi-strain virus caused by birds and mammals.…”

Mobile Applications for the Implementation of Health Control against Covid-19 in Educational Centers, a Systematic Review of the Literature

“…Availability and functionality [12], [25], [30], [31], [34], [35], [38], [48], [56], [63], [69], [74], [77], [79], [82], [88], [92], [105], [112], [115], [117], [120], [128], [133], [136] Availability, functionality, and reliability [24], [40], [66], [73], [80], [87], [100], [114] Availability, functionality, and performance [11], [16], [29], [46], [62], [86], [110] Availability, functionality, and security [37], [44], [58], [64], [95], [122], [137] Functionality [14], [15],…”

“…[12], [25], [30], [31], [34], [35], [38], [48], [56], [63], [69], [74], [77], [79], [82], [88], [92], [105], [112], [115], [117], [120], [128], [133], [136] These articles take into account the quality attributes of availability, functionality and reliability, taking into account this, it will be possible to develop software for sanitary control against Covid-19 with high-quality standards.…”

The role of the California tier system in controlling population mobility during the COVID- 19 pandemic

“…We identi ed 20 papers that have investigated this relationship, which are provided in Table S1. Seven of these studies assessed the impact of these policies on mobility within the United States (U.S.) only (20)(21)(22)(23)(24), while thirteen have explored this relationship in other parts of the world including China, Canada, Poland, Italy, France, Hungary, Greece, and on a global scale (5,12,(33)(34)(35)(36)(37)(25)(26)(27)(28)(29)(30)(31)(32). Certain countries, including China, the United Kingdom, South Africa, and Israel, utilized regionallevel, real-time evidence to implement various levels of mobility restriction to reduce the spread of COVID- 19 (38-40).…”