纽约市最初爆发COVID-19期间的基本商务访问和社会脆弱性。

Debra F Laefer, Delphine Protopapas
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引用次数: 2

摘要

2020年春季,纽约市受到COVID-19的严重影响,每天新增数千例病例。然而,大流行的影响并没有均匀地分布在整个城市,具体的贡献者尚未得到系统的考虑。为了帮助调查这一主题,本研究通过匿名手机数据分析了2020年春季第一次COVID-19浪潮高峰期纽约三个社区部分地区人们与社区企业和其他兴趣点(poi)的互动,并指导观察了1313名离开医疗机构的人。本研究考虑了社会脆弱性指数(SVI)水平、人口密度和POI访问行为,这些行为来自手机数据和对纽约市不同地区选定的卫生机构周围行为的第一手观察,作为各种代理。通过按社区考虑同等企业或企业群体,POI访问比SVI更符合COVID-19感染水平。如果在与更大的数据集核对时,跟踪POI访问水平被证明是疾病传播的可靠直接或相对代理,那么这种方法在预测未来疫情和设定客户密度限制方面可能至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Essential Business Visits and Social Vulnerability during New York City's Initial COVID-19 Outbreak.

Essential Business Visits and Social Vulnerability during New York City's Initial COVID-19 Outbreak.

Essential Business Visits and Social Vulnerability during New York City's Initial COVID-19 Outbreak.

Essential Business Visits and Social Vulnerability during New York City's Initial COVID-19 Outbreak.

New York City (NYC) was deeply impacted by COVID-19 in spring 2020, with thousands of new cases daily. However, the pandemic's effects were not evenly distributed across the city, and the specific contributors have not yet been systematically considered. To help investigate that topic, this study analyzed the interaction of people with neighborhood businesses and other points of interest (POIs) in parts of three NYC neighborhoods in the spring of 2020 during the peak of the first COVID-19 wave through anonymized cellphone data and direct the observation of 1313 individuals leaving healthcare facilities. This study considered social vulnerability index (SVI) levels, population density, and POI visit behaviors from both cellphone data and firsthand observations of behavior around select NYC health facilities in different boroughs as various proxies. By considering equivalent businesses or groups of businesses by neighborhood, POI visits better aligned with COVID-19 infection levels than SVI. If tracking POI visit levels proves a reliable direct or relative proxy for disease transmission when checked against larger datasets, this method could be critical in both predictions of future outbreaks and the setting of customer density limits.

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CiteScore
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