Spatially Explicit Modeling of 2019-nCoV Epidemic Trend based on Mobile Phone Data in Mainland China

Xiaolin Zhu; Aiyin Zhang; Shuai Xu; Pengfei Jia; Xiaoyue Tan; Jiaqi Tian; Tao Wei; Zhenxian Quan; Jiali Yu

We collected the daily data of confirmed cases of 2019-nCoV Pneumonia in 306 prefecture-level cities in mainland China up to February 11, 2020 (Supplementary data Table 1 ) from a platform reporting real-time statistics of 2019-nCoV (https://ncov.dxy.cn/ncovh5/view/pneumonia). These daily reported data were used to train and validate our epidemic model. We employed China Unicom mobile phone database (https://www.cubigdata.cn) to obtain the inter-city human mobility. China Unicom is one of three largest mobile service providers in China. It has 0.32 billion users. Considering that 2019-nCoV emerged in Wuhan around January 1, 2020 and Wuhan implemented the quarantine on January 23, 2020, we collected the number of people who have Wuhan travel history during January 1-24, 2020 in each city based on the mobile phone dataset ( Figure 2 and Supplementary data Table 2 ). In addition, Household Registered Population at 2017 year-end derived from census data was used to approximate the number of local residents in each city during 2020 Spring Festival (Supplementary data Table 2 ). A spatially explicit epidemic model