Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA.
Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD, USA.
Lancet Infect Dis. 2020 Nov;20(11):1247-1254. doi: 10.1016/S1473-3099(20)30553-3. Epub 2020 Jul 1.
Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge.
We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county.
Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35-63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9-12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message.
This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future.
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在美国首次报告 COVID-19 病例后的 4 个月内,该病毒已传播至每个州和超过 90%的县。在此期间,美国的 COVID-19 应对措施高度分散,州和地方官员发布了就地避难指令,但执行力度各不相同。由于缺乏集中的政策和时间表,再加上人类流动性的复杂动态以及局部疫情的强度不同,评估大规模社会隔离措施对美国 COVID-19 传播的影响是一项挑战。
我们使用来自 Teralytics(瑞士苏黎世)的汇总和匿名手机数据(蜂窝)每日移动数据,从 2020 年 1 月 1 日至 4 月 20 日,捕获每个美国县实时的移动模式趋势,并使用这些数据生成社会隔离指标。我们使用流行病学数据计算给定县在给定日期的 COVID-19 增长率比。使用这些指标,我们评估了在 2020 年 4 月 16 日确诊病例数最多的 25 个美国县中,通过为每个县拟合统计模型,以移动性的相对变化衡量的社会隔离措施如何影响新感染的速度。
我们的分析表明,对于美国受影响最严重的县,移动模式与 COVID-19 病例增长率的下降密切相关,在评估的 25 个县中,有 20 个县的皮尔逊相关系数高于 0.7。此外,移动模式变化的影响(与正常情况相比下降了 35-63%)可能需要 9-12 天甚至最多 3 周才能被察觉,这与严重急性呼吸综合征冠状病毒 2 的潜伏期加上报告所需的额外时间相符。我们还提供了证据表明,在州级或地方级就地避难政策实施之前,许多美国县的行为改变已经进行了数天至数周,这意味着尽管政治信息混杂,但个人还是预料到了公共卫生指令中会采用社会隔离措施。
这项研究强烈支持社会隔离是减轻美国 COVID-19 传播的有效方法。在广泛提供 COVID-19 疫苗之前,社会隔离仍将是控制疾病传播的主要措施之一,这些发现应该有助于未来在美国更及时地制定社会隔离政策。
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