Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach

Abstract

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.

Random forest using smartphone GPS in first wave of COVID-19 in the Maule region, Chile

“…Hence the importance of quarantines in the early stage of the pandemic; total mobility, active cases, external mobility, and internal mobility are the most relevant variables in VIM in the RF model used. Similar situations were observed in the studies in the Americas, Europe, and Africa (6)(7)(8). Larger and more populated cities have a lower external mobility index when the index is adjusted for population size.…”

An analysis of the spatio-temporal behavior of COVID-19 patients using activity trajectory data

“…However, a multinational study [ 48 ] with data from several countries, including the EU and the US, showed that changes in mobility in places such as restaurants, cafes, grocery stores, transit stations, and parks played a more important role in reducing disease transmission than in workplaces or residential areas. Data from Florida [ 49 ] also showed that the increase in average visits to bars and restaurants was one of the main factors contributing to the increase in COVID-19 cases. There may be two main reasons for these differences.…”

A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach

“…In addition to sociodemographic factors, there are also factors specific to the COVID-19 pandemic that could be relevant. Some of these factors include human mobility behaviors such as average home-dwelling time that increased due to stay-at-home orders, and warnings about levels of infection ( Xiong et al, 2020 ; Zhu et al, 2021 ). To understand the increase in opioid deaths during the pandemic, we have accounted for COVID-19 impacts in the analysis by including COVID-19 positive cases and COVID-19 deaths, and social distance metrics (e.g., average home dwelling time, percentage of people staying at home, percentage of work behavior device) as additional indicators for estimating opioid-involved deaths during COVID-19 ( Ghose et al, 2022 ; Huang et al, 2021 ).…”