Optimized Hybrid Deep Learning for Real-Time Pandemic Data Forecasting: Long and Short-Term Perspectives


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Abstract

Background:With new variants of COVID-19 causing challenges, we need to focus on integrating multiple deep-learning frameworks to develop intelligent healthcare systems for early detection and diagnosis.

Objective:This article suggests three hybrid deep learning models, namely CNN-LSTM, CNN-Bi- LSTM, and CNN-GRU, to address the pressing need for an intelligent healthcare system. These models are designed to capture spatial and temporal patterns in COVID-19 data, thereby improving the accuracy and timeliness of predictions. An output forecasting framework integrates these models, and an optimization algorithm automatically selects the hyperparameters for the 13 baselines and the three proposed hybrid models.

Methods:Real-time time series data from the five most affected countries were used to test the effectiveness of the proposed models. Baseline models were compared, and optimization algorithms were employed to improve forecasting capabilities.

Results:CNN-GRU and CNN-LSTM are the top short- and long-term forecasting models. CNNGRU had the best performance with the lowest SMAPE and MAPE values for long-term forecasting in India at 3.07% and 3.17%, respectively, and impressive results for short-term forecasting with SMAPE and MAPE values of 1.46% and 1.47%.

Conclusion:Hybrid deep learning models, like CNN-GRU, can aid in early COVID-19 assessment and diagnosis. They detect patterns in data for effective governmental strategies and forecasting. This helps manage and mitigate the pandemic faster and more accurately.

About the authors

Sujata Dash

Information Technology Department, School of Engineering & Technology, Nagaland University

Email: info@benthamscience.net

Sourav Giri

Computer Application, Maharaja Srirama Chandra Bhanja Deo University

Email: info@benthamscience.net

Subhendu Pani

Computer Science & Engineering, Krupajal Computer Academy,

Email: info@benthamscience.net

Saurav Mallik

Department of Environmental Health, Harvard T.H. Chan School of Public Health,, Harvard University

Author for correspondence.
Email: info@benthamscience.net

Mingqiang Wang

Department of Biomedicine, Stanford University

Email: info@benthamscience.net

Hong Qin

Department of Computer Science & Engineering, University of Tennessee at Chattanooga

Author for correspondence.
Email: info@benthamscience.net

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