Deep Ensemble Learning Method to Forecast COVID-19 Outbreak

Due to the continuous spread of the novel coronavirus (COVID-19) worldwide, it is urgent to develop accurate decision-aided methods to support healthcare policymakers to control and early detect COVID-19 outbreak especially in the data science era. In this context, our main goal is to build a generic and accurate method that can predict daily conrmed cases which helps stake-holders to make and review their epidemic response plans. This method takes advantage of the complementarity of DNN (Deep Neuronal Networks), LSTM (Long Short Term Memory) and CNN (Convolutional Neuronal Networks) where their forecasted values represent the inputs of stacked ensemble meta-learners that will generate the nal outbreak predictions. To the best of our knowledge, this is the rst time that deep ensemble learning is used to deal with this issue. The proposed method is validated on three experimental scenarios, Tunisia case study, China case study and the third one is based on China data and models to predict Tunisia COVID-19 outbreak. Experiment results indicate that, compared with individual learners, the stacked-DNN meta-learner, whose input are forecasted values of DNN, LSTM and CNN, achieved the best accurate results in terms of accuracy as well as RMSE for the three scenarios. In conclusion, our ndings demonstrate that i) deep ensemble learning may be used as an accurate decision support tool for improving COVID-19 outbreak forecasting, ii) it is possible to reuse China learners and meat-learners to make prediction of the epidemic trend for other countries when preventive and control measures are comparable.