Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. (November 2020)

• Record Type: Journal Article
• Authors: Shahid, Farah; Zameer, Aneela; Muneeb, Muhammad
• Is Part Of: Chaos, solitons and fractals. Volume 140(2020)
• Publication Date: 2020-11
• Subjects: Deep learning models -- Bi-LSTM -- GRU -- Corona virus -- COVID-19 -- epidemic prediction; SIR Susceptible-infective-removed -- WHO World health organization -- SARS Severe acute respiratory syndrome -- MERS Middle East respiratory syndrome -- SVR Support vector machine -- ARIMA Autoregressive integrated moving average -- AR Autoregressive -- SARIMA Seasonal autoregressive integrated moving average -- AI Artificial intelligence -- NN Neural network -- DL Deep learning -- LSTM Long short term memory -- GRU Gated recurrent network -- RF Random forest -- Bi-LSTM Bidirectional long short term memory -- RNN Recurrent neural network; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2020.110212
• Languages: English
• ISSNs: 0960-0779

Abstract

Abstract: COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.