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

  1. Keke Tang
  2. Yining Huang
  3. Meilian Chen

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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 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.

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    SciScore for 10.1101/2020.02.20.20023572: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2020.02.20.20023572v2 on medRxiv
  3. Version published to 10.1101/2020.02.20.20023572v1 on medRxiv