Additional parameters can be customized in the *Sim inputs* tab.
Results
Plot
Table
Customize simulation parameters and then press "re-run simulation" below.
Options
Quarantine durations (days)
Probabilities
Test sensitivity
Tool summary
This tool is intended to estimate the impact of policies to manage the risk of
COVID-19 introduced by arriving travelers. The policies compared are mandatory
quarantine of varying length with or without testing. The user can customize
several simulation parameters related to compliance with quarantine and isolation, test
sensitivity, the relative prevalence of asymptomatic vs. infection. Uncertainty in outcomes
reflect uncertainty in the distribution of the duration of the
pre-symptomatic-infectious and symptomatic-infectious periods for those with symptomatic
infections, the duration of the asymptomatic-infectious period for those with asymptomatic
infections, and the incubation period for both infection types
Users can toggle between five metrics. The metric "Days at-risk per
infected traveler" refers to the average time an infected traveler would be at risk of
infecting members of the community because they are infectious and not in quarantine or
isolation. The metric "Adjusted days at-risk per infected travelers" multiplies the
infectious days in the community for those with asymptomatic infections by the relative
transmission risk for asymptomatic infections, which is less than 1 in the base case.
This reflects the belief that symptomatic infections have a higher community transmission risk.
The primary metric "Percent risk reduced" is calculated
by comparing the adjusted days at-risk for each policy to that of a 0-day quarantine
without testing policy.
While these first 3 metrics require only the parameters from the simulation, two others
require additional parameters. The outcome "Person-days at risk per 10,000 travelers"
has both infected and uninfected travelers
in the denominator. For this outcome, the user must indicate an estimated or assumed
prevalence of active infection among travelers. The outcome "Secondary cases" estimates how
many community members you would expect to be infected by travelers. Calculating this outcome
requires an estimate of the rate of secondary infections per person-day that an traveler is infectious and
at-risk in the community.
About the authors
This tool was created by Alton Russell, postdoctoral research fellow at the Mass General
Hospital Institute for Technology Assessment and Harvard Medical School and visitor
at McGill Clinical & Health Informatics (MCHI), in collaboration
with David Buckeridge, Professor of Epidemiology and Biostatistics at McGill University
and director of the surveillance lab at MCHI.
For questions, feel free to email altonr <at> stanford <dot> edu.
A huge thanks to Aman Verma and Maxime Lavigne for technical assistance with hosting the app.