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Novel Coronavirus 2019 (Covid-19) epidemic scale estimation: topological network-based infection dynamic model
Preprint
in English
| medRxiv
| ID: ppmedrxiv-20023572
ABSTRACT
BackgroundsAn 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.
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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Prognostic study
/
Rct
Language:
English
Year:
2020
Document type:
Preprint
