| Title | Prediction of the COVID-19 epidemic trends based on SEIR and AI models |
| Authors | Feng, Shuo Feng, Zebang Ling, Chen Chang, Chen Feng, Zhongke |
| Affiliation | Peking Univ, Sch Software & Microelect, Beijing, Peoples R China Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China Beijing Forestry Univ, Beijing Key Lab Precis Forestry, Beijing, Peoples R China |
| Issue Date | 8-Jan-2021 |
| Publisher | PLOS ONE |
| Abstract | In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic. |
| URI | http://hdl.handle.net/20.500.11897/609974 |
| ISSN | 1932-6203 |
| DOI | 10.1371/journal.pone.0245101 |
| Indexed | SCI(E) |
| Appears in Collections: | 软件与微电子学院 |
