Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
By Zi-feng Yang, Zhiqi Zeng, Ke Wang, Sook-san Wong, W. Liang, Mark Zanin, Peng Liu, Xudong Cao, Zhong-Ke Gao, Zhitong Mai and 16 more. Journal of Thoracic Disease, 2020
1,285 citations recorded by the source corpus.
Background The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.
Subfields: Information Systems, Modeling and Simulation, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/9670485f526f2254c0f34e64d9ca06f665a0bd17
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Citations in the corpus
- Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics (2020, neutral): “Many scholars have developed a number of predicting methds for the trend forecasting of COVID-19, in some severe counries and global [8,9] , debating about mathematical model, infecious disease model, and artificial intelligence model.”
- Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM (2020, positive): “Another popular statistical model in the field of health care system is known as artificial intelligence (AI) based which is used to learn and train the COVID-19 dataset of Hubei Province in China to predict the epidemic peaks and trend size [13] .”
- A SIR model assumption for the spread of COVID-19 in different communities (2020, positive): “Modelling approaches [8, 16, 17, 18, 19, 20, 21] are helpful to understand and predict the possibility and severity of the disease outbreak and, provide key information to determine the intensity of COVID-19 disease intervention.”