O'Donoghue Ashley, Dechen Tenzin, Pavlova Whitney, Boals Michael, Moussa Garba, Madan Manvi, Thakkar Aalok, DeFalco Frank J, Stevens Jennifer P
Center for Healthcare Delivery Science, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Department of Statistics, Pennsylvania State University, University Park, PA, USA.
NPJ Digit Med. 2021 Mar 16;4(1):51. doi: 10.1038/s41746-021-00420-9.
The true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level (P < 0.001). We found that an increase in a county's average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1-1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.
随着各州重新开放商业活动,新冠疫情卷土重来的真正风险尚不明朗。在本文中,我们使用匿名手机数据,通过构建一个衡量随时间变化的传播风险的商业风险指数,来量化新冠病毒在商业场所传播的潜在风险。该指数是利用每平方英尺的访问量和平均访问时长这两个指标构建的,以兼顾访问密度和访客在商业场所停留的时间长度。我们分析了2020年1月至2020年6月期间八个州1,272,260家企业的交通模式趋势。我们发现,到4月时,企业潜在的高风险交通行为减少了30%。自4月底以来,随着各州重新开放,风险指数一直在上升。各州和各行业的趋势存在一些显著差异。最后,我们表明,平均商业风险指数的时间序列对于预测县级未来的新冠病例很有用(P < 0.001)。我们发现,一个县的平均商业风险指数上升与1周内新冠阳性病例增加相关(发病率比值比:1.16,95%置信区间:(1.1 - 1.2))。我们的风险指数为政策制定者和医院决策者提供了一种方法,可根据对企业的访问频率和密度来监测新冠病毒在企业中传播的潜在风险。在各州监测和评估其重新开放策略时,这可作为一项重要指标。