Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data.
Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data.
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DOI：
10.7554/elife.80466
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发表时间：
2023-04-04
期刊：
影响因子：
7.7
通讯作者：
Bansal S
中科院分区：
文献类型：
--
作者：
Susswein Z;Rest EC;Bansal S
自2019冠状病毒病疫情爆发以来，大量公众关注集中于季节性因素在影响传播方面的作用。误解依赖于仅由环境变量驱动的呼吸道疾病的季节性调节。然而，季节性预计将由主机的社会行为，特别是在高度易感人群。在理解社会行为在呼吸道疾病季节性中的作用方面的一个关键差距是我们对室内人类活动的季节性的不完全理解。我们利用一种关于人类流动性的新数据流来描述美国室内与室外环境中的活动。我们使用了一个基于观测移动的应用程序的位置数据集，涵盖了全国超过500万个位置。我们将地点主要分类为室内（例如商店，办公室）或室外（例如游乐场，农贸市场），将特定地点的访问分解为室内和室外，以获得跨时间和空间的室内到室外人类活动的精细尺度测量。我们发现，在基准年期间，室内活动与室外活动的比例是季节性的，在冬季达到峰值。该措施显示了纬度梯度与较强的季节性在北方纬度和一个额外的夏季高峰在南纬度。我们在统计学上拟合了这一基线室内-室外活动指标，以将这一复杂的经验模式纳入传染病动态模型。然而，我们发现，COVID-19大流行的破坏导致这些模式从基线显著转变，经验模式对于预测疾病动态的时空异质性是必要的。我们的工作经验的特点，第一次，在一个大规模的高时空分辨率的人类社会行为的季节性，并提供了一个简约的参数化的季节性行为，可以包括在传染病动力学模型。我们提供必要的关键证据和方法，为公众健康提供季节性和大流行性呼吸道病原体的信息，并提高我们对全球变化背景下物理环境与感染风险之间关系的理解。本出版物中报告的研究得到了美国国立卫生研究院国家普通医学科学研究所的支持，奖励编号为R 01 GM 123007。
Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in impacting transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use an observational mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visits into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform the incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline and the empirical patterns are necessary to predict spatiotemporal heterogeneity in disease dynamics. Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large scale with a high spatiotemporal resolutio and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global change. Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM123007.