Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19

  1. Allison K. Shaw
  2. Lauren A. White
  3. Matthew Michalska-Smith
  4. Elizabeth T. Borer
  5. Meggan E. Craft
  6. Eric W. Seabloom
  7. Emilie C. Snell-Rood
  8. Michael Travisano

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Abstract

Human behavior (movement, social contacts) plays a central role in the spread of pathogens like SARS-CoV-2. The rapid spread of SARS-CoV-2 was driven by global human movement, and initial lockdown measures aimed to localize movement and contact in order to slow spread. Thus, movement and contact patterns need to be explicitly considered when making reopening decisions, especially regarding return to work. Here, as a case study, we consider the initial stages of resuming research at a large research university, using approaches from movement ecology and contact network epidemiology. First, we develop a dynamical pathogen model describing movement between home and work; we show that limiting social contact, via reduced people or reduced time in the workplace are fairly equivalent strategies to slow pathogen spread. Second, we develop a model based on spatial contact patterns within a specific office and lab building on campus; we show that restricting on-campus activities to labs (rather than labs and offices) could dramatically alter (modularize) contact network structure and thus, potentially reduce pathogen spread by providing a workplace mechanism to reduce contact. Here we argue that explicitly accounting for human movement and contact behavior in the workplace can provide additional strategies to slow pathogen spread that can be used in conjunction with ongoing public health efforts.

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  1. Version published to 10.1371/journal.pone.0242955
  2. Version published to 10.1101/2020.05.27.20114728v3 on medRxiv
  3. Version published to 10.1101/2020.05.27.20114728v2 on medRxiv
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    SciScore for 10.1101/2020.05.27.20114728: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

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    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Then we ran the following simulations that varied some parameters while holding others constant: Movement model simulations were conducted in Matlab 2018b.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  5. Version published to 10.1101/2020.05.27.20114728v1 on medRxiv