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Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

Devaraj, J., Madurai Elavarasan, R., Pugazhendhi, R., Shafiullah, GM.ORCID: 0000-0002-2211-184X, Ganesan, S., Jeysree, A.K., Khan, I.A. and Hossain, E. (2021) Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant? Results in Physics, 21 . Art. 103817.

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Abstract

The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID-19 cases, multivariate LSTM models is employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% as compared to the other considered algorithms for the studied performance metrics. Country-specific analysis and city-specific analysis of COVID-19 cases for India and Chennai, respectively, are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID-19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs).

Item Type: Journal Article
Murdoch Affiliation(s): Engineering and Energy
Publisher: Elsevier B.V.
Copyright: © 2021 The Author(s).
United Nations SDGs: Goal 3: Good Health and Well-Being
URI: http://researchrepository.murdoch.edu.au/id/eprint/59546
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