Skip to main content
Dryad logo

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

Citation

Shaw, Allison et al. (2020), Data from: Lessons from movement ecology for the return to work: modeling contacts and the spread of COVID-19, Dryad, Dataset, https://doi.org/10.5061/dryad.pg4f4qrmj

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.

Usage Notes

README.txt: This text file contains readme and notes for all files.
 

Funding

University of Minnesota’s Office of Academic Clinical Affairs COVID-19 Rapid Response Grant

National Science Foundation, Award: DEB-2030509

National Science Foundation, Award: DEB-1654609

National Science Foundation, Award: DEB-1556649