Disentangling the rhythms of human activity in the built environment for airborne transmission risk: an analysis of large-scale mobility data

  1. Zachary Susswein
  2. Eva C Rest
  3. Shweta Bansal  Is a corresponding author
  1. 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

  1. Zachary Susswein

    Department of Biology, Georgetown University, Washingston, United States
    Competing interests
    Zachary Susswein, is currently employed at the Rockefeller Foundation as a Data Analyst. The author has no other competing interests to declare..
  2. Eva C Rest

    Department of Biology, Georgetown University, Washingston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6461-3450
  3. Shweta Bansal

    Department of Biology, Georgetown University, Washingston, United States
    For correspondence
    shweta.bansal@georgetown.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1740-5421

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.

Reviewing Editor

  1. Niel Hens, Hasselt University, Belgium

Version history

  1. Preprint posted: April 16, 2022 (view preprint)
  2. Received: May 21, 2022
  3. Accepted: March 20, 2023
  4. Accepted Manuscript published: April 4, 2023 (version 1)
  5. Version of Record published: April 20, 2023 (version 2)

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.

Metrics

  • 627
    Page views
  • 93
    Downloads
  • 3
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Zachary Susswein
  2. Eva C Rest
  3. Shweta Bansal
(2023)
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

Share this article

https://doi.org/10.7554/eLife.80466

Further reading

    1. Epidemiology and Global Health
    David Robert Grimes
    Research Advance Updated

    In biomedical science, it is a reality that many published results do not withstand deeper investigation, and there is growing concern over a replicability crisis in science. Recently, Ellipse of Insignificance (EOI) analysis was introduced as a tool to allow researchers to gauge the robustness of reported results in dichotomous outcome design trials, giving precise deterministic values for the degree of miscoding between events and non-events tolerable simultaneously in both control and experimental arms (Grimes, 2022). While this is useful for situations where potential miscoding might transpire, it does not account for situations where apparently significant findings might result from accidental or deliberate data redaction in either the control or experimental arms of an experiment, or from missing data or systematic redaction. To address these scenarios, we introduce Region of Attainable Redaction (ROAR), a tool that extends EOI analysis to account for situations of potential data redaction. This produces a bounded cubic curve rather than an ellipse, and we outline how this can be used to identify potential redaction through an approach analogous to EOI. Applications are illustrated, and source code, including a web-based implementation that performs EOI and ROAR analysis in tandem for dichotomous outcome trials is provided.

    1. Epidemiology and Global Health
    Qixin He, John K Chaillet, Frédéric Labbé
    Research Article

    The establishment and spread of antimalarial drug resistance vary drastically across different biogeographic regions. Though most infections occur in sub-Saharan Africa, resistant strains often emerge in low-transmission regions. Existing models on resistance evolution lack consensus on the relationship between transmission intensity and drug resistance, possibly due to overlooking the feedback between antigenic diversity, host immunity, and selection for resistance. To address this, we developed a novel compartmental model that tracks sensitive and resistant parasite strains, as well as the host dynamics of generalized and antigen-specific immunity. Our results show a negative correlation between parasite prevalence and resistance frequency, regardless of resistance cost or efficacy. Validation using chloroquine-resistant marker data supports this trend. Post discontinuation of drugs, resistance remains high in low-diversity, low-transmission regions, while it steadily decreases in high-diversity, high-transmission regions. Our study underscores the critical role of malaria strain diversity in the biogeographic patterns of resistance evolution.