Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, Boston, MA, USA.
Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, Boston, MA, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Lancet Digit Health. 2022 Jan;4(1):e27-e36. doi: 10.1016/S2589-7500(21)00214-4. Epub 2021 Nov 2.
In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA.
In this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (R) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of R values with mobility proxies.
We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [-0·492 to -0·392]), consistent with the emergence of more complex local policies and behaviours, including masking.
Our study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and R. Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health.
There was no funding source for this study.
2020 年初，针对 SARS-CoV-2 大流行的应对措施侧重于非药物干预，其中一些措施旨在通过改变人与人之间的混合模式来减少传播。来自移动电话的聚合位置数据是人群水平上有关人类流动性的实时信息的重要来源，但这些流动性指标在多大程度上捕捉到了有传播 SARS-CoV-2 风险的个人的相关接触模式尚不清楚。在这项研究中，我们描述了美国移动电话数据与 SARS-CoV-2 传播之间关系的变化。
在这项基于人群的研究中，我们从美国 1396 个县收集了有关 COVID-19 病例和死亡的流行病学数据，以及从全球定位系统衍生的由广告技术汇总的人类流动性指标。这些县被分为六个有序类别，由国家卫生统计中心（NCHS）定义，从城市到农村分为不同等级，并使用 2020 年 1 月 22 日至 7 月 9 日之间估计的有效繁殖数（R）量化 COVID-19 传播的变化，以调查聚集流动性指标与流行轨迹之间的关系。对于每个县，我们使用流动性代理模型来对 R 值的时间序列进行建模。
我们表明，繁殖数与进入各县的旅行变化的流动性代理最密切相关（0.757 [95%CI 0.689 至 0.857]），但这种关系主要适用于 NCHS 定义的三个最城市化的县。这种关系在疫情的最初 15 周后大大减弱（0.442 [-0.492 至 -0.392]），这与更复杂的当地政策和行为的出现一致，包括戴口罩。
我们的研究表明，将流动性指标纳入回顾性建模工作中可以帮助识别这些指标与 R 之间的联系。重要的是，我们强调了从移动电话数据中得出的传播指标的数据生成过程、代表性和获取公平性方面存在的问题，这些问题必须得到解决，以提高这些数据在公共卫生中的可解释性。
本研究没有资金来源。
