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#COVID spreads through human-to-human transmission, so #migrants are an important vector. In the absence of adequate covid tests in LMICs, can we predict sub-national COVID spread, or identify likely hotspots using data on migration?

Short answer: Yes.
yrise.yale.edu/using-migratio…
Data on airport returnees predict subsequent quarantines & #COVID19 distress calls across districts in #Bangladesh. Data on migration permits predict confirmed cases in #Philippines municipalities and Bangladeshi sub-districts.
Beyond the validation using public health data, our recent phone surveys across Bangladesh helps to ground-truth this approach:
Living in communities with recent #migrant returnees triples the odds (!) of reporting #COVID symptoms. This is the single largest risk factor.
We provide a proof of concept to create sub-national #COVID heat maps, that can inform aid targeting, or the design of #zoning, #reopening & smart containment policies. Approach can be applied to any #LMIC that has #LSMS-style @worldbankdata. Or to identify high-risk countries
All 5 authors from 🇧🇩, produced this for 🇧🇩 policymakers: @rnahsan_eco, @Kazixiqbal, @mahreen06, @Abu_Shonchoy.

@YaleRISE happy to help other LMICs create these sub-national heat maps for designing zoning policies & smart containment strategies, if that is of use.
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