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.
Highlights
Since January 2020, when the first confirmed case of the SARS-CoV-2 coronavirus disease (COVID-19) was reported in the United States, the pandemic has ravaged the United States, with the number of confirmed cases and deaths at over 30.2 million and551,000, respectively, as of March 2021 [1]
As the COVID-19 pandemic impacted the daily lives of individuals, this research found that, based on tracking inflow trips at census tract level for three counties in Florida, mobility was impacted by COVID-19, especially when compared to mobility during the pre-COVID period
The set of key explanatory factors revealed by the random forest model were travel-related factors and built environment factors, while sociodemographic factors were present
Summary
Since January 2020, when the first confirmed case of the SARS-CoV-2 coronavirus disease (COVID-19) was reported in the United States, the pandemic has ravaged the United States, with the number of confirmed cases and deaths at over 30.2 million and551,000, respectively, as of March 2021 [1]. The movement of people as they go about their daily lives or travel over larger spatial extents (e.g., travel by air) has been a key focus of study, throwing a spotlight on the role of mobility in sustaining the level of infection and transmission [2,3]. We use a random forest regression model to determine how a set of more than 30 different factors, including sociodemographic (e.g., median household income, age, race, and ethnicity), travel (e.g., mean travel time to work, percent of the population working from home), and built environment factors (e.g., road network density, street intersection density), as well as the changing number of COVID-19 positive cases, relates to changing levels of mobility across the tri-county region. Our study is at the detailed granularity of census tracts, highlighting how human behaviors relating to mobility across tracts and between counties varied over time and space, and providing insights for planning, as well as possible consequences for pandemic outcomes
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