Exposure density and neighborhood disparities in COVID-19 infection risk

Boyeong Hong, Bartosz J. Bonczak, Arpit Gupta, Lorna E. Thorpe and Constantine E. Kontokosta
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Boyeong Hong: Marron Institute of Urban Management, New York University, New York, NY 10011
Bartosz J. Bonczak: Marron Institute of Urban Management, New York University, New York, NY 10011
Arpit Gupta: Stern School of Business, New York University, New York, NY 10012
Lorna E. Thorpe: Department of Population Health, New York University School of Medicine, New York, NY 10016
Constantine E. Kontokosta: Marron Institute of Urban Management, New York University, New York, NY 10011; Center for Urban Science and Progress, New York University, Brooklyn, NY 11201

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 13, e2021258118

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 ρ ) 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.

Keywords: mobility behavior; neighborhood disparities; COVID-19; computational modeling; geolocation data (search for similar items in EconPapers)
Date: 2021
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