Harris Jeffrey E
Professor of Economics, Emeritus, Massachusetts Institute of Technology, Cambridge MA 02139; Physician, Eisner Health, Los AngelesCA90015. Email:
Epidemiol Infect. 2022 Apr 5;150:1-31. doi: 10.1017/S0950268822000498.
We combined smartphone mobility data with census track-based reports of positive case counts to study a coronavirus disease 2019 (COVID-19) outbreak at the University of Wisconsin–Madison campus, where nearly 3000 students had become infected by the end of September 2020. We identified a cluster of twenty bars located at the epicentre of the outbreak, in close proximity to campus residence halls. Smartphones originating from the two hardest-hit residence halls (Sellery-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-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 [rate ratio 1.91, 95% confidence interval (CI) 1.29–2.85, < 0.001]. We also 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 21 census tracts in the university area. In a multivariate instrumental variables regression, the visitation rate to the bar cluster was a significant determinant of the per-capita incidence of positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests in each census tract (elasticity 0.88, 95% CI 0.08–1.68, = 0.032), while the restaurant visitation rate showed no such relationship. The potential super-spreader effects of clusters or networks of places, rather than individual sites, require further attention.
我们将智能手机移动性数据与基于人口普查区的确诊病例数报告相结合,以研究威斯康星大学麦迪逊分校校园内的2019冠状病毒病(COVID-19)疫情,截至2020年9月底,该校近3000名学生被感染。我们在疫情中心确定了一组20家酒吧,它们紧邻校园宿舍。来自受影响最严重的两个宿舍(塞勒里-维特)的智能手机,约五分之一的学生被感染,与来自距离更远、受影响较小的两个宿舍(奥格-史密斯)的智能手机相比,访问这20家酒吧集群的可能性高出2.95倍。相比之下,塞勒里-维特的智能手机访问同一地区68家餐厅的可能性仅比奥格-史密斯的智能手机高出1.55倍[率比1.91,95%置信区间(CI)1.29–2.85,<0.001]。我们还确定了来自大学区域内21个人口普查区的智能手机访问这20家酒吧集群和68家餐厅对照组的人均比率。在多变量工具变量回归中,访问酒吧集群的比率是每个人口普查区严重急性呼吸综合征冠状病毒2(SARS-CoV-2)检测呈阳性的人均发病率的一个重要决定因素(弹性0.88,95%CI 0.08–1.68,=0.032),而访问餐厅的比率则没有这种关系。场所集群或网络而非单个场所的潜在超级传播效应需要进一步关注。