Disentangling the rhythms of human activity in the built environment for airborne transmission risk: an analysis of large-scale mobility data
- Georgetown University, United States
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusions: Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large scale with high spatiotemporal resolution, 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.
Funding: 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.
Data availability
We make available on Github the data and code needed to reproduce all figures and analyses in this manuscript: https://github.com/bansallab/indoor_outdoor. The dataset we provide is of the metric used in all our analyses and figures ("indoor activity"). This dataset can be regenerated using the Safegraph Weekly Patterns datasets found at https://docs.safegraph.com/docs/weekly-patterns and code in the Github repository.The Safegraph Weekly Patterns was made freely available to academics at a uniquely granular level in response to the COVID-19 pandemic. Safegraph's business model involves selling these datasets to other corporations and, as a result, any data access agreement with the company forbids sharing of the raw data. The company does, however, make its data freely available to academics (for non-commercial use) through an institutional university subscription to Dewey or an individual data use agreement with Safegraph.
Article and author information
Author details
Funding
National Institutes of Health (R01GM123007)
- Zachary Susswein
- Eva C Rest
- Shweta Bansal
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Ethical review for this study was sought from the Institutional Review Board at Georgetown University and the study was approved on October 14, 2020 (STUDY00003041). This is secondary data analysis, so no informed consent or consent to publish was necessary.
Copyright
© 2023, Susswein et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Disentangling the rhythms of human activity in the built environment for airborne transmission risk: an analysis of large-scale mobility data
eLife 12:e80466.
https://doi.org/10.7554/eLife.80466
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