The Prospects of Multi-modal Pre-trained Models in Epidemic Forecasting

Author:

Fei Jiaqiang,Zhao Pengfei,Luo Tianyi,Wang Jiaojiao,Cao Zhidong

Publisher

Springer Nature Singapore

Reference28 articles.

1. Al-Raeei, M.: The forecasting of COVID-19 with mortality using SIRD epidemic model for the United States, Russia, China, and the Syrian Arab Republic. AIP Adv. 10(6) (2020)

2. Ala’raj, M., Majdalawieh, M., Nizamuddin, N.: Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections. Infect. Dis. Model. 6, 98–111 (2021)

3. Bousquet, A., Conrad, W.H., Sadat, S.O., Vardanyan, N., Hong, Y.: Deep learning forecasting using time-varying parameters of the SIRD model for COVID-19. Sci. Rep. 12(1), 3030 (2022)

4. LNCS;Q Cao,2022

5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

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