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

Cite this dataset

Shaw, Allison et al. (2020). Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19 [Dataset]. Dryad.


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.

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

This dataset is from the paper titled "Lessons from movement ecology for the return to work: modeling contacts and the spread of COVID-19"

By: Allison K. Shaw, Lauren A. White, Matthew Michalska-Smith,
Elizabeth T. Borer, Meggan E. Craft, Eric W. Seabloom, Emilie Snell-Rood,
Michael Travisano

Published in: PLoS One

Contact for assistance.

The files included here are from the 'movement model' from the paper;
please see elsewhere for files for the 'network model' from the paper.

The following files are included:



SEIR.m: Calculate instantaneous rate of change for susceptibles (S),
exposed (E), infected (I), and removed (R) in a simple SEIR
model with frequency-dependent transmission dynamics.

run_SEIR_spatial.m: runs simulations of a semi-spatial SIR model,
tracking the number of susceptibles (S), exposed (E),

infected (I), and removed (R) individuals. All individuals
spend a fraction of each day (T_h) at home, a fraction (T_w)
working either from home or from work and a fraction (2*T_c)
commuting to and from work or at home.

fig2_physicaldistancing.m: runs simulations for figure 2 (creates fig2.jpg)

fig3a_run.m, fig3b_run.m, fig3c_run.m: run simulations for a number of
different parameter combinations (creates fig3a_tw_theta.mat,
fig3b_R0work_theta.mat, fig3c_R0work_tw.mat)

fig3_plot.m: plot the resuts a number of different parameter
combinations (creates fig3.jpg)


set_parameters.m sets baseline parameters

set_LHSdistributions.m generates LHS distributions

set_parameters_monotonicity.m sets up parameters for monotonicity check

setup_LHS.m sets up LHS matrix

check_LHS_distributions.m check that parameter distributions are

check_monotonicity_metric1.m checks monotonicity for metric 1

check_monotonicity_metric2.m checks monotonicity for metric 2

check_number_runs.m determines number of runs to use

check_PRCCvals_rank.m check rank of PRCC value for significance

run_analysis.m MAIN script to run LHS and PRCC analysis

Downloaded from

LHS_Call.m generates sampling of all parameters

PRCC.m calculates PRCC values


fig3a_tw_theta.mat: data (.mat) file, generated by fig3a_run.m, used to make fig3

fig3b_R0work_theta.mat: data (.mat) file, generated by fig3b_run.m, used to make fig3

fig3c_R0work_tw.mat: data (.mat) file, generated by fig3c_run.m, used to make fig3

output_MonoPlots1.mat: data (.mat) file, generated by check_monotonicity_metric1.m

output_MonoPlots2.mat: data (.mat) file, generated by check_monotonicity_metric2.m

output_numruns.mat: data (.mat) file, check_number_runs.m

output_PRCC.mat: data (.mat) file, generated by run_analysis.m

Usage notes

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


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