Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China; School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China.
School of Traffic and Transportation Engineering, Central South University, Hunan, 410075, PR China.
Comput Biol Med. 2022 Jul;146:105560. doi: 10.1016/j.compbiomed.2022.105560. Epub 2022 Apr 27.
The COVID-19 outbreak poses a huge challenge to international public health. Reliable forecast of the number of cases is of great significance to the planning of health resources and the investigation and evaluation of the epidemic situation. The data-driven machine learning models can adapt to complex changes in the epidemic situation without relying on correct physical dynamics modeling, which are sensitive and accurate in predicting the development of the epidemic. In this paper, an ensemble hybrid model based on Temporal Convolutional Networks (TCN), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN), Q-learning, and Support Vector Machine (SVM) models, namely TCN-GRU-DBN-Q-SVM model, is proposed to achieve the forecasting of COVID-19 infections. Three widely-used predictors, TCN, GRU, and DBN are used as elements of the hybrid model ensembled by the weights provided by reinforcement learning method. Furthermore, an error predictor built by SVM, is trained with validation set, and the final prediction result could be obtained by combining the TCN-GRU-DBN-Q model with the SVM error predictor. In order to investigate the forecasting performance of the proposed hybrid model, several comparison models (TCN-GRU-DBN-Q, LSTM, N-BEATS, ANFIS, VMD-BP, WT-RVFL, and ARIMA models) are selected. The experimental results show that: (1) the prediction effect of the TCN-GRU-DBN-Q-SVM model on COVID-19 infection is satisfactory, which has been verified in three national infection data from the UK, India, and the US, and the proposed model has good generalization ability; (2) in the proposed hybrid model, SVM can efficiently predict the possible error of the predicted series given by TCN-GRU-DBN-Q components; (3) the integrated weights based on Q-learning can be adaptively adjusted according to the characteristics of the data in the forecasting tasks in different countries and multiple situations, which ensures the accuracy, robustness and generalization of the proposed model.
新型冠状病毒肺炎疫情的爆发对国际公共卫生构成了巨大挑战。对病例数量进行可靠预测，对于卫生资源规划和疫情调查评估具有重要意义。基于数据驱动的机器学习模型可以适应疫情的复杂变化，而无需依赖正确的物理动力学建模，在预测疫情发展方面具有敏感性和准确性。本文提出了一种基于时间卷积网络（TCN）、门控循环单元（GRU）、深度置信网络（DBN）、Q-learning 和支持向量机（SVM）模型的集成混合模型，即 TCN-GRU-DBN-Q-SVM 模型，用于实现新型冠状病毒肺炎感染的预测。该混合模型集成了三个广泛使用的预测器，即 TCN、GRU 和 DBN，作为由强化学习方法提供权重的混合模型的元素。此外，使用 SVM 构建误差预测器，通过验证集进行训练，最终通过将 TCN-GRU-DBN-Q 模型与 SVM 误差预测器相结合来获得预测结果。为了研究所提出的混合模型的预测性能，选择了几个比较模型（TCN-GRU-DBN-Q、LSTM、N-BEATS、ANFIS、VMD-BP、WT-RVFL 和 ARIMA 模型）进行比较。实验结果表明：（1）TCN-GRU-DBN-Q-SVM 模型对新型冠状病毒肺炎感染的预测效果令人满意，在来自英国、印度和美国的三个国家的感染数据中得到了验证，该模型具有良好的泛化能力；（2）在提出的混合模型中，SVM 可以有效地预测 TCN-GRU-DBN-Q 组件给出的预测序列的可能误差；（3）基于 Q-learning 的集成权重可以根据不同国家和多种情况下的预测任务的数据特征进行自适应调整，从而保证了所提出模型的准确性、鲁棒性和泛化性。
