Fine scale human mobility changes in 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income

Abstract

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level mobility data from 26 US cities between February 2 - August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June - August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Socioeconomic correlates of urban mobility trends in two Australian cities during transitional periods of the COVID-19 pandemic

“…through support policies such as the economic support packages provided by the Australian Government during the initial period of stay-at-home orders, and we credit these with the notable lack of correlation between ER and mobility changes. We observed that all income strata demonstrated similarly large decreases in mobility during this crucial period, in contrast to findings from other contexts [15][16][17].…”

“…Studies that use a single aggregate measure of SES have reported that mobility or behaviour change is the least pronounced for those with low SES [12][13][14]. Studies that consider the components of SES indices have found differing trends of mobility with the SES components, such as a decrease in mobility with income [15][16][17], decrease in mobility with education but no variation with income [18,19], decrease in mobility with income but no variation with education level [20] and decrease in mobility with both wealth and occupational status [21]. While such studies demonstrate that socioeconomic conditions are important considerations for understanding and predicting behavioural responses to interventions, the lack of consensus illustrates the contextual complexity of these questions.…”

Characterizing US spatial connectivity: implications for geographical disease dynamics and metapopulation modeling

“…Since the onset of the COVID-19 pandemic, mobile phone data has played a crucial role in addressing the public health crisis [27,38,35,32,20,26,34,22,43,40,29,21,19,18]. During this period, numerous network operators and private enterprises have made considerable efforts to swiftly share their data within the confines of legal regulations.…”

“…This data was based on mobile location-based app usage and thus provided incomparable access to high-resolution, large-scale, and near-real-time mobility data and has expanded human mobility science [37], and computational epidemiology [19,18]. The availability of this data has especially represented a shift in US public health and it has been used to inform epidemic models and reveal the impact of mitigation strategies on behavior [27,38,35,32,20,26,15,25]. While the association between mobility patterns and COVID-19 transmission in the USA has been extensively studied, no studies have been devoted to assessing when the underlying mobility network needs to be embedded into models to characterize epidemic spread.…”