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《MedRxiv，2月23日，Novel Coronavirus 2019 (Covid-19) epidemic scale estimation: topological network-based infection dynamic model》

  • 来源专题：COVID-19科研动态监测
  • 编译者： xuwenwhlib
  • 发布时间：2020-02-24

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

    Keke Tang, Yining Huang, Meilian Chen

    doi: https://doi.org/10.1101/2020.02.20.20023572

    Abstract

    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 used historical data and SEIR model for important parameters assumption. And according to the timeline, we used dynamic parameters for infection topology network building. Also, the migration data was used for the Non-Wuhan area estimation which can be cross-validation for the Wuhan model. All data were from the public. Results: The estimated number of infections was 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), infection scale in Guangzhou was 315 (95%CI: 109.20-520.79), while the imported cases were 168 and the scale of the infection was 339 published by the authority. Conclusions: dynamic network model and dynamic parameter for the different time periods is an effective way for infection scale modeling.

    *注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.

  • 原文来源：https://www.medrxiv.org/content/10.1101/2020.02.20.20023572v1
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