Author: Xiaolin Zhu; Aiyin Zhang; Shuai Xu; Pengfei Jia; Xiaoyue Tan; Jiaqi Tian; Tao Wei; Zhenxian Quan; Jiali Yu
Title: Spatially Explicit Modeling of 2019-nCoV Epidemic Trend based on Mobile Phone Data in Mainland China Document date: 2020_2_11
ID: 6u9q0ox9_13
Snippet: We predicted the epidemic dynamics in the next 30 days under three scenarios: the current trend maintained (scenario 1), control efforts expanded (scenario 2), person-to-person contacts increased due to work resuming (scenario 3). These three scenarios were designed by considering the joint effect of virus transmissibility and outbreak control 18, 19 and realized by manipulating model parameters from February 11, 2020 to March 12, 2020 (see an ex.....
Document: We predicted the epidemic dynamics in the next 30 days under three scenarios: the current trend maintained (scenario 1), control efforts expanded (scenario 2), person-to-person contacts increased due to work resuming (scenario 3). These three scenarios were designed by considering the joint effect of virus transmissibility and outbreak control 18, 19 and realized by manipulating model parameters from February 11, 2020 to March 12, 2020 (see an example in Figure 3 ). Decay rate reveals the effectiveness of government control and removal rate represents the promptness of medical treatment. Therefore, scenario 1 keeps a value the same as the trained model. In scenario 2, we doubled the value of a to reflect more efforts in each city for controlling the disease. In scenario 3, the interference of work resuming was considered, so a short rebound was introduced to the transmission rate (i.e. changing a to -a during February 11-15 period). in all scenarios gradually increased from 1/14.3 to 1/9.8 in the 30 days to reflect the reduction of average diagnostic isolation time 20 .
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