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Can we assess where travel restrictions will be most effective for internal control of COVID? A new analysis by @cmmid_lshtm with Mark Jit, @samclifford, Joesph Wu, @AdamJKucharski and John Edmunds attempts to (https://cmmid.github.io/topics/covid19/Travel-Restrictions.html …). FYI Not peer reviewed! 1/npic.twitter.com/CPndsH9WrP
We combine prevalence estimates and incidence estimates, calculated according to the methods outlined here (https://cmmid.github.io/topics/covid19/Under-Reporting.html …) and here (https://cmmid.github.io/topics/covid19/Under-Reporting.html …), with detailed travel data to estimate the risk of imported cases relative to local incidence 2/n
We adjust the travel data downward to represent flight numbers during COVID. We do so using the @OpenSkyNetwork monthly databases, which allow us to calculate flight path specific reduction factors. We present the relative risk for each country in the form of a map 3/n
Our prevalence and incidence estimates are relatively crude, making global assumptions so that an analysis like this is possible (see the SI of the associated manuscript here: https://cmmid.github.io/topics/covid19/reports/global_travel_restrictions/supplementary.pdf … for a verbose description of the limitation and caveats of these estimates) 4/n
But we are able to rigorously incorporate uncertainty in the model, so we present the lower and upper 95% credible interval of the relative risk estimates in the form of a grid of 12 maps! Full credit to @samclifford for the figures and data vis aspects of the work! 5/5pic.twitter.com/koY8MOak51
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