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| Based on Time-varying Parameters |
| LI Feng, BIN Sheng, SUN Gengxin
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| School of Computer Science and Technology, Qingdao University, Qingdao 266071, China |
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Abstract Novel coronavirus is a new type of virus, and its transmission characteristics are different from previous virus. Infected people not only have an incubation period, but also a large number of asymptomatic infections. Based on the classic model SEIR, this study redefines the latent state as close contact state, introduces an asymptomatic state of infection, and the influence of time on the state transition parameters in the model is considered, proposed a new transmission model which includes five types of states: susceptible state, close contact state, asymptomatic infection state, infected state, and removed state. The model uses the actual epidemic data of Hubei Province to conduct experiments, and uses RMSE and MAPE as evaluation indicators to compare the experimental results. The results show that the fitting accuracy of the SCUIR model has been significantly improved. Compared with the traditional model, the fitting error is reduced by 8.3%~47.6%, and hidden data that is difficult to count in the epidemic can be calculated, which further characterizes the mechanism of epidemic transmission.
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Received: 26 April 2021
Published: 23 May 2022
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