Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study
Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study
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DOI：
10.1016/s1473-3099(20)30553-3
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发表时间：
2020-11-01
影响因子：
56.3
通讯作者：
Gardner, Lauren M.
中科院分区：
文献类型：
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作者：
Badr, Hamada S.;Du, Hongru;Gardner, Lauren M.
背景 在美国首次报告 COVID-19 后的 4 个月内，它就传播到了每个州以及 90% 以上的县。在此期间，美国的 COVID-19 应对措施高度分散，各州和地方官员发布了居家指令，但执行力度各不相同。由于缺乏集中化的政策和时间表，加上人员流动的复杂动态和局部疫情的不同强度，评估大规模社交距离对美国 COVID-19 传播的影响成为一项挑战。方法我们使用来自 Teralytics（瑞士苏黎世）2020 年 1 月 1 日至 4 月 20 日期间汇总和匿名手机数据的每日流动数据来捕获实时流动性数据 美国每个县的运动模式趋势，并使用这些数据生成社交距离指标。我们使用流行病学数据来计算特定县在特定日期的 COVID-19 增长率。使用这些指标，我们通过拟合每个县的统计模型，评估了以流动性相对变化来衡量的社交距离如何影响 2020 年 4 月 16 日美国确诊病例数最多的 25 个县的新感染率。结果我们的分析显示，流动性模式与美国受影响最严重的县的 COVID-19 病例增长率下降密切相关，皮尔逊相关系数高于 评估的 25 个县中，有 20 个县的得分为 0.7。此外，相对于正常情况下降了 35-63% 的流动模式变化对 COVID-19 传播的影响在 9-12 天内不太可能被察觉，甚至可能长达 3 周，这与严重急性呼吸综合征冠状病毒 2 的潜伏时间加上额外的报告时间一致。我们还表明，有证据表明，在州级或地方级居家政策实施前几天到几周，美国许多县的行为就已经发生了变化，这意味着尽管政治信息复杂，但个人还是预期会采取社交距离的公共卫生指令。 解释 这项研究强烈支持社交距离作为减轻美国 COVID-19 传播的有效方法的作用。在广泛使用 COVID-19 疫苗之前，社交距离仍将是对抗疾病传播的主要措施之一，这些发现应有助于支持美国未来围绕社交距离制定更及时的政策。版权所有 (C) 2020 爱思唯尔有限公司。保留所有权利。
Background 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.Methods We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zurich, 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.Findings 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.Interpretation 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. Copyright (C) 2020 Elsevier Ltd. All rights reserved.