WITHDRAWN: Deep Ensemble Learning Method to Forecast COVID-19 Outbreak

Abstract

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.

Applying Deep Learning Methods on Time‐Series Data for Forecasting COVID‐19 in Egypt, Kuwait, and Saudi Arabia

“…Many scientific articles have appeared about forecasting COVID-19. For example, Yahia et al [13] proposed a new technique using deep neural networks, long short-term memory, and CNN to forecast daily confirmed cases. Hu et al [14] proposed new methods by using artificial intelligence for real-time forecasting of COVID-19 to evaluate the size, lengths, and ending time of COVID-19 across China.…”

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

“…While the presented results show good fitness with the existing data, the authors incorrectly predict the end of the pandemic no later than the beginning of May 2020, which is apparently an incorrect prediction. To predict the number of COVID-19 confirmed cases daily, Yahia et al [ 122 ] propose a deep ensemble learning method. Such an ensemble consists of deep ANNs, Long Short-Term Memory networks, and Convolutional ANNs, thereby, the advantage of each algorithm can be used to improve forecasting results.…”

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

“…The emphasis was on various towns with the most reported cases in China, and a COVID-19 prognostication model was suggested based upon the CNN system of Deep Neural Network (DNN). Three deep learning models ( 20 ), namely DNN, LSTM, and CNN, were stacked in learning models for the ensemble to achieve the most reliable results. The meta-learners used these forecasted values of these models as inputs to produce the final prediction of outbreaks.…”