Author: Pablo M De Salazar; Rene Niehus; Aimee Taylor; Caroline O Buckee; Marc Lipsitch
Title: Using predicted imports of 2019-nCoV cases to determine locations that may not be identifying all imported cases Document date: 2020_2_5
ID: 9fd5a49o_5
Snippet: is the author/funder, who has granted medRxiv a license (which was not peer-reviewed) The copyright holder for this preprint .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 cities and countries becoming epicentres of the 2019-nCoV epidemic, substantial targeted public health interventions are required, first for detection of cases and then for control of local spread. Here we use estimates of air travel volume from Wuhan to in.....
Document: is the author/funder, who has granted medRxiv a license (which was not peer-reviewed) The copyright holder for this preprint .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 cities and countries becoming epicentres of the 2019-nCoV epidemic, substantial targeted public health interventions are required, first for detection of cases and then for control of local spread. Here we use estimates of air travel volume from Wuhan to international destinations in 49 locations expected to be proficient at detecting imported cases based on having a high Global Health Security (GHS) Index 3 , and a generalised linear regression model to predict imports of 2019-nCoV cases across 191 locations. Using these predictions we can identify locations that might not be identifying imported cases.
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