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Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

1
Department of Computer Science, National University of Technology, Islamabad 44000, Pakistan
2
Department of Computer Science, Umm Al-Qura University, Makkah 24236, Saudi Arabia
3
Department of Computer Engineering, Umm Al-Qura University, Makkah 24236, Saudi Arabia
*
Author to whom correspondence should be addressed.
Computers 2020, 9(4), 99; https://doi.org/10.3390/computers9040099
Received: 25 October 2020 / Revised: 6 December 2020 / Accepted: 8 December 2020 / Published: 11 December 2020
COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin. View Full-Text
Keywords: COVID-19; lockdown; hotspots; CNN; LSTM; prediction COVID-19; lockdown; hotspots; CNN; LSTM; prediction
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MDPI and ACS Style

Khan, S.D.; Alarabi, L.; Basalamah, S. Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network. Computers 2020, 9, 99. https://doi.org/10.3390/computers9040099

AMA Style

Khan SD, Alarabi L, Basalamah S. Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network. Computers. 2020; 9(4):99. https://doi.org/10.3390/computers9040099

Chicago/Turabian Style

Khan, Sultan D., Louai Alarabi, and Saleh Basalamah. 2020. "Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network" Computers 9, no. 4: 99. https://doi.org/10.3390/computers9040099

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