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Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

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  • Shahid, Farah
  • Zameer, Aneela
  • Muneeb, Muhammad

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.

Suggested Citation

  • Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920306081
    DOI: 10.1016/j.chaos.2020.110212
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    Cited by:

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    18. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Anna Kulakowska & Anna Zylka & Karolina Grabowska & Katarzyna Ciesielska & Wojciech Nowak, 2020. "Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)," Energies, MDPI, vol. 13(24), pages 1-16, December.
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