Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study

Shengjie Lai; Isaac Bogoch; Nick Ruktanonchai; Alexander Watts; Xin Lu; Weizhong Yang; Hongjie Yu; Kamran Khan; Andrew J Tatem

As we used historial data to predict travel patterns in 2020, to ensure that the seasonal patterns observed over LNY and holidays more generally are consistent over multiple years and countries, we collated country-level domestic and international passenger statistics for air travel from 2010 to 2018, and compared these against a comprehensive time series of public and school holidays across the world during the same period (Supplementary Note). Additionally, we also compared the spatial patterns of the risks of Chinese cities importing the virus from Wuhan, estimated by the population movement data in 2014 and 2015, and more recent data on the top 50 ranked origin and destination cities in January 2020, available from the Baidu Migration site (https://qianxi.baidu.com/). To futher validate our results, we also compared the importation risk estimated in this study with the number of reported imported cases from Wuhan to other provinces in mainland China, as of January 25 th , 2020, and the number of imported cases reported by other countries or regions, as of February 3 rd , 2020 [6] . The distribution between days of travelling from Wuhan, illness onset, first medical visit, and hospitalization of imported cases, as of January 25 th , 2020, were also analysed. These case data were collated from the websites of WHO, national and local health authorities or new agencies within and beyond China (Supplementary Note). R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria) was used to perform data analyses.