Geospatial Analysis of the September 2020 Coronavirus Outbreak at the University of Wisconsin – Madison: Did a Cluster of Local Bars Play a Critical Role?

J. Harris
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引用次数: 14

Abstract

We combined smartphone mobility data with census track-based reports of positive case counts to study a coronavirus outbreak at the University of Wisconsin-Madison campus, where nearly three thousand students had become infected by the end of September 2020. We identified a cluster of twenty bars located at the epicenter of the outbreak, in close proximity to on-campus residence halls and off-campus housing. Smartphones originating from the two hardest hit residence halls (Sellery and Witte), where about one in five students were infected, were 2.95 times more likely to visit the 20-bar cluster than smartphones originating in two more distant, less affected residence halls (Ogg and Smith). By contrast, smartphones from Sellery-Witte were only 1.55 times more likely than those from Ogg-Smith to visit a group of 68 restaurants in the same area. Physical proximity thus had a much stronger influence on bar visitation than on restaurant visitation (rate ratio 1.91, 95% CI 1.29-2.85, p = 0.0007). In a separate analysis, we determined the per-capita rates of visitation to the 20-bar cluster and to the 68-restaurant comparison group by smartphones originating in each of 19 census tracts in the university area, and related these visitation rates to the per-capita incidence of newly positive coronavirus tests in each census tract. In a multivariate regression, the visitation rate to the bar cluster was a significant determinant of infection rates (elasticity 0.90, 95% CI 0.26-1.54, p = 0.009), while the restaurant visitation rate showed no such relationship. Researchers and public health professionals need to think more about the potential super-spreader effects of clusters and networks of places, rather than individual sites.
威斯康星大学麦迪逊分校2020年9月冠状病毒爆发的地理空间分析:一群当地酒吧是否发挥了关键作用?
我们将智能手机移动数据与基于人口普查跟踪的阳性病例数报告结合起来,研究了威斯康星大学麦迪逊分校的冠状病毒爆发,到2020年9月底,该校已有近3000名学生被感染。我们确定了一个由20家酒吧组成的集群,位于疫情中心,靠近校园宿舍和校外宿舍。来自两个受影响最严重的宿舍(Sellery和Witte)的智能手机,大约有五分之一的学生被感染,与来自两个更远、受影响较小的宿舍(Ogg和Smith)的智能手机相比,来自这两个宿舍的智能手机访问20酒吧群的可能性要高2.95倍。相比之下,来自selley - witte的智能手机访问同一地区68家餐厅的可能性仅为Ogg-Smith的1.55倍。因此,物理距离对酒吧光顾的影响比对餐馆光顾的影响要大得多(比率比1.91,95% CI 1.29-2.85, p = 0.0007)。在另一项分析中,我们确定了大学地区19个普查区中每个普查区的智能手机对20个酒吧群和68个餐馆对照组的人均访问率,并将这些访问率与每个普查区新冠状病毒检测阳性的人均发病率联系起来。在多元回归中,酒吧群的访问量是感染率的显著决定因素(弹性0.90,95% CI 0.26-1.54, p = 0.009),而餐馆的访问量没有这种关系。研究人员和公共卫生专业人员需要更多地考虑集群和地方网络的潜在超级传播者效应,而不是单个站点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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