Interstates of Infection: Preliminary Investigations of Human Mobility Patterns in the COVID‐19 Pandemic

Abstract

Purpose The COVID‐19 pandemic has illuminated various heterogeneities between urban and rural environments in public health. The SARS‐CoV‐2 virus initially spread into the United States from international ports of entry and into urban population centers, like New York City. Over the course of the pandemic, cases emerged in more rural areas, implicating issues of transportation and mobility. Additionally, many rural areas developed into national hotspots of prevalence and transmission. Our aim was to investigate the preliminary impacts of road travel on the spread of COVID‐19. This investigation has implications for future public health mitigation efforts and travel restrictions in the United States. Methods County‐level COVID‐19 data were analyzed for spatiotemporal patterns in time‐to‐event distributions using animated choropleth maps. Data were obtained from The New York Times and the Bureau of the Census. The arrival event was estimated by examining the number of days between the first reported national case (January 21, 2020) and the date that each county attained a given prevalence rate. Of the 3108 coterminous US counties, 2887 were included in the analyses. Data reflect cases accumulated between January 21, 2020, and May 17, 2020. Findings Animations revealed that COVID‐19 was transmitted along the path of interstates. Quantitative results indicated rural–urban differences in the estimated arrival time of COVID‐19. Counties that are intersected by interstates had an earlier arrival than non‐intersected counties. The arrival time difference was the greatest in the most rural counties and implicates road travel as a factor of transmission into rural communities. Conclusion Human mobility via road travel introduced COVID‐19 into more rural communities. Interstate travel restrictions and road travel restrictions would have supported stronger mitigation efforts during the earlier stages of the COVID‐19 pandemic and reduced transmission via network contact.

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant — Shanghai Municipality, China, 2022

“…Initially, the outbreak spread in and around the inner ring. Previous literature also found that the COVID-19 pandemic in the United States was characterized by a geographically localized mosaic of transmission along an urban-rural gradient ( 8 - 9 ), suggesting that geographic distance may play an important role in SARS-CoV-2 spread. Finally, the public health impact of COVID-19 vaccines has already been widely discussed in the literature ( 10 - 13 ) and our study confirms previous evidence.…”

A standardised differential privacy framework for epidemiological modelling with mobile phone data

“…In reviewing epidemiological models using mobility data, we identified salient metrics of interest, including probability of infection [12], risk of importation [13], incubation period [15], mean importation rate [14], size at the epidemic peak [29], effective reproduction number [11], epidemic size [22], proportion of counties with at least one case [30], rate of spread [31], timing of the peak [32] and the average size of the peak [33].…”

Rural infection preventionists’ experiences during the COVID-19 pandemic: Findings from focus groups conducted with association of professionals in infection control & epidemiology (APIC) members

“…4 However, as the pandemic evolved, cases were identified in rural areas, stemming from human road travel. 5 Some rural areas even became hotspots of disease or the source of focused outbreaks, such as in meat processing plants located in rural areas. 6 The pandemic created healthcare surges nationwide, including in urban, suburban, and rural areas, and posed significant challenges for infection preventionists (IP) who are charged with identifying, preventing, and controlling infectious disease transmission in healthcare settings.…”