Novel Coronavirus 2019 (Covid-19) epidemic scale estimation: topological network-based infection dynamic model
Tang, K.; Huang, Y.; Chen, M.
Show 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.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 95%
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 94%
- Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm 94%
Similar papers in this journal
- The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran 95%
- Analysis of the early Covid-19 epidemic curve in Germany by regression models with change points 91%
- The burden of isolation to the individual: a comparison between isolation for COVID-19 and for other influenza-like illnesses in Japan 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.