Exposure density and neighborhood disparities in COVID-19 infection risk

Significance

We present a computational approach to measure exposure density at high spatial and temporal resolution to understand neighborhood disparities in transmission risk of COVID-19. By integrating geolocation data and granular land-use information, we are able to establish both the extent of activity in a particular neighborhood and the nature of that activity across residential, nonresidential, and outdoor activities. We then analyze the differential behavioral response to social-distancing policies based on local risk factors, built-environment characteristics, and socioeconomic inequality. Our results highlight the significant disparities in health outcomes for racial and ethnic minorities and lower-income households. Exposure density provides an additional metric to further explain and understand the disparate impact of COVID-19 on vulnerable communities.

Abstract

Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social-distancing policies vary with socioeconomic and demographic characteristics. We define exposure density ( $E_x\rho$ ) as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities.