Using predicted imports of 2019-nCoV cases to determine locations that may not be identifying all imported cases

Pablo M De Salazar; Rene Niehus; Aimee Taylor; Caroline O Buckee; Marc Lipsitch

Under a model fit to data from destinations with high surveillance capacity, we sought to identify locations that report fewer than predicted imported cases of 2019-nCoV. Specifically, for 49 locations with The model requires three types of data: data on imported cases of 2019-nCoV, data on daily air travel volume, and data on surveillance capacity. Data on imported cases aggregated by destination were obtained from the WHO technical report dated 4th February 2020 2 (a zero case count was assumed for all locations not listed). We used case counts up to the 4th February, because after this date the number of exported cases from Hubei drops rapidly 2 , likely due to the Hubei-wide lockdowns. We defined imported cases as those with known travel history from China (of those, 83% had travel history from Hubei province, and 17% from unknown locations in China 2 ). We excluded cases that are likely due to transmission outside of China or whose transmission source is still under investigation 2 . In addition, we excluded from our Specifically, we use the Early Detection and Reporting Epidemics of Potential International Concern component of the Index 3 , henceforth referred to as simply the GHS index, and define high surveillance locations as those whose GHS index is greater than the 75th quantile. is the author/funder, who has granted medRxiv a license (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.04.20020495 doi: medRxiv preprint