论文标题
COVID-19感染风险的暴露密度和邻居差异:使用大规模的地理位置数据了解弱势社区的负担
Exposure Density and Neighborhood Disparities in COVID-19 Infection Risk: Using Large-scale Geolocation Data to Understand Burdens on Vulnerable Communities
论文作者
论文摘要
这项研究开发了一种新方法,以量化高空间和时间分辨率下的邻里活动水平,并测试对社会距离政策的行为反应是否随社会经济和人口统计学特征而异。我们将暴露密度定义为衡量定义区域中局部活性的量度以及在非住宅和户外土地用途中发生的活动比例。我们利用这种方法来捕获大流行的流入/流出,以及剩下的人的流动行为变化。首先,我们开发了一种可推广的方法,用于在三个月内使用智能手机地理位置数据评估土地利用类型,以覆盖大纽约地区的1200万唯一用户。其次,我们通过确定在家中和之后的邻里活动水平和特征中的模式来衡量和分析社区社会疏远的差异。最后,我们评估了社区中社会距离对与局部人口统计,社会经济和基础设施特征相关的COVID-19感染率以及结果的影响,以确定与暴露风险有关的健康结果的差异。我们的发现提供了对及时评估社会疏远对单个社区的有效性的见解,并支持更公平的资源分配,以支持脆弱和处于危险的社区。我们的发现表明了各个社区的活动前和杂化活动的不同模式。暴露密度的变化对感染风险有直接且可测量的影响。
This study develops a new method to quantify neighborhood activity levels 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 as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in non-residential and outdoor land uses. We utilize this approach to capture inflows/outflows of people as a result of the pandemic and changes in mobility behavior for those that remain. First, we develop a generalizable method for assessing neighborhood activity levels by land use type using smartphone geolocation data over a three-month period covering more than 12 million unique users within the Greater New York area. Second, we measure and analyze disparities in community social distancing by identifying patterns in neighborhood activity levels and characteristics before and after the stay-at-home order. Finally, we evaluate the effect of social distancing in neighborhoods on COVID-19 infection rates and outcomes associated with localized demographic, socioeconomic, and infrastructure characteristics in order to identify disparities in health outcomes related to exposure risk. Our findings provide insight into the timely evaluation of the effectiveness of social distancing for individual neighborhoods and support a more equitable allocation of resources to support vulnerable and at-risk communities. Our findings demonstrate distinct patterns of activity pre- and post-COVID across neighborhoods. The variation in exposure density has a direct and measurable impact on the risk of infection.