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[Preprint]. 2020 Feb 11:2020.02.04.20020495.
doi: 10.1101/2020.02.04.20020495.

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

P M De Salazar  1 , R Niehus  1 , A Taylor  1 , C Buckee  1 , M Lipsitch  1
Affiliations

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

P M De Salazar et al. medRxiv. .

Update in

Abstract

Cases from the ongoing outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV) exported from mainland China can lead to self-sustained outbreaks in other populations. Internationally imported cases are currently being reported in several different locations. Early detection of imported cases is critical for containment of the virus. Based on air travel volume estimates from Wuhan to international destinations and using a generalized linear regression model we identify locations which may potentially have undetected internationally imported cases.

Keywords: coronavirus; outbreak; pneumonia; travel.

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Figures

Figure 1
Figure 1. Imported case counts and daily air travel volume.
The solid line shows the expected case counts based on our model fitted to high surveillance locations (purple points), and the dashed lines show the 95% prediction interval bounds for all locations including those that do not have high surveillance (light blue points). Cluster A (Cl.A) comprises locations with one imported case reported and air travel <10 passengers per day: Nepal, Sri Lanka, Finland, and Sweden. Cluster B (Cl.B) comprises locations (n=161) with no reported cases and estimated air travel <10 passengers per day. Other abbreviations: Germany (Ge), New Zealand (NZ), United Arab Emirates (UAE), United States of America (USA), United Kingdom (UK), Russia (Ru).
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