Novel Coronavirus 2019 (Covid-19) epidemic scale estimation: topological network-based infection dynamics model

Backgrounds An ongoing outbreak of novel coronavirus pneumonia (Covid-19) hit Wuhan and hundreds of cities, 29 territories in global. We present a method for scale estimation in dynamic while most of the researchers used static parameters. Methods We use historical data and SEIR model for important parameters assumption. And according to the time line, we use dynamic parameters for infection topology network building. Also, the migration data is used for Non-Wuhan area estimation which can be cross validated for Wuhan model. All data are from public. Results The estimated number of infections is 61,596 (95%CI: 58,344.02-64,847.98) by 25 Jan in Wuhan. And the estimation number of the imported cases from Wuhan of Guangzhou was 170 (95%CI: 161.27-179.26), infections scale in Guangzhou is 315 (95%CI: 109.20-520.79), while the imported cases is 168 and the infections scale is 339 published by authority. Conclusions Using dynamic network model and dynamic parameters for different time periods is an effective way for infections scale modeling.