Author:
Tang Keke,Huang Yining,Chen Meilian
Abstract
AbstractBackgroundsAn 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.MethodsWe 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.ResultsThe 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.ConclusionsUsing dynamic network model and dynamic parameters for different time periods is an effective way for infections scale modeling.
Publisher
Cold Spring Harbor Laboratory
Reference11 articles.
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