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

We aimed to identify locations with underdetected cases by fitting a model to the cumulative international imported case counts of nCoV-2019 reported by high surveillance locations and Wuhan-to-location air travel volume. Our model can be adjusted to account for exportation of cases from locations other than Wuhan as the outbreak develops and more information on importations and self-sustained transmission becomes available. One key advantage of this model is that it does not rely on estimates of incidence or prevalence in the epicentre of the outbreak. Based on our model, locations whose case counts exceed the 95% prediction interval (PI) could be interpreted as having higher case-detection capacity and/or more connection with Wuhan than that captured by available daily air travel volume, such as land transportation. Locations below the 95% PI may have undetected cases based on the expected case count under high surveillance. We recommend that outbreak surveillance and control efforts for potential local transmission should be rapidly strengthened in those locations lying below the 95% PI lower bound, in particular Indonesia, to ensure detection of cases and appropriate control measures to reduce the risk of self-sustained transmission.