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

Projecting the epidemic trend of 2019-nCoV outbreak is critical for the decision makers to allocate resources and take appropriate actions to control virus transmission. Right after the outbreak, several studies have retrieved the epidemiological parameters and predicted the future situation [12] [13] [14] [15] . These studies used the reported cases at the early stage of outbreak and modeled epidemic dynamics in Wuhan and nation-wide. A recent study 10 used air passenger data and social medium data to forecast the spread of 2019-nCoV in Wuhan and other major Chinese cities. They estimated that 75815 individuals have been infected in Greater Wuhan as of January 25, 2020 and epidemics are already growing exponentially in major cities of China with a 1-2 weeks lag time behind Wuhan outbreak. Although these studies at the early stage of outbreak help us understand the key epidemiological characteristics of 2019-nCoV, the fine-scale and updated epidemic trend in individual Chinese cities remains unknown, which is more helpful for allocating medical resources to achieve the optimal result of preventing disease spreading.