DOI: 10.5061/dryad.pg4f4qrmj
title: Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19

description: 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.
description: README.txt: This text file contains readme and notes for all
 files.<br> 
description: # 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 ashaw@umn.edu 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: (1) files_runcode.zip
 MOVEMENT MODEL SIMULATION SCRIPTS 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) MOVEMENT MODEL PRCC ANALYSIS
 SCRIPTS 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 uniform
 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
 http://malthus.micro.med.umich.edu/lab/usadata/ LHS_Call.m generates
 sampling of all parameters PRCC.m calculates PRCC values (1)
 files_output.zip 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