Journal
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
Volume 16, Issue 2, Pages 276-288Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2022.3152375
Keywords
COVID-19; Predictive models; Mathematical models; Data models; Pandemics; Market research; Biological system modeling; COVID-19; generative adversarial networks; time series prediction; SIR simulation
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Funding
- Zhejiang Provincial Key Research and Development Program of China [2021C01106]
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University
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Studying the spread and predicting the epidemic trend of COVID-19 is crucial for global control measures. However, current epidemiological and machine learning models have limitations in accurate prediction. In this study, we propose the T-SIRGAN model, which integrates epidemiological theories and deep learning models to accurately predict the growth trend of COVID-19. Extensive experiments demonstrate the superiority of our method.
The Coronavirus disease 2019 (COVID-19) is a respiratory illness that can spread from person to person. Since the COVID-19 pandemic is spreading rapidly over the world and its outbreak has affected different people in different ways, it is significant to study or predict the evolution of its epidemic trend. However, most of the studies focused solely on either classical epidemiological models or machine learning models for COVID-19 pandemic forecasting, which either suffer from the limitation of the generalization ability and scalability or the lack of surveillance data. In this work, we propose T-SIRGAN that integrates the strengths of the epidemiological theories and deep learning models to be able to represent complex epidemic processes and model the non-linear relationship for more accurate prediction of the growth of COVID-19. T-SIRGAN first adopts the Susceptible-Infectious-Recovered (SIR) model to generate epidemiological-based simulation data, which are then fed into a generative adversarial network (GAN) as adversarial examples for data augmentation. Then, Transformers are used to predict the future trends of COVID-19 based on the generated synthetic data. Extensive experiments on real-world datasets demonstrate the superiority of our method. We also discuss the effectiveness of vaccine based on the difference between the predicted and the reported number of COVID-19 cases.
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