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Application of optimized LSTM in prediction of the cumulative confirmed cases of COVID-19

Author

Listed:
  • M. He
  • W. W. Zhu
  • H. Z. Chen
  • Hongbing Zhu

Abstract

This paper proposes an optimized Long Short-Term Memory (LSTM+) model for predicting cumulative confirmed cases of COVID-19 in Germany, the UK, Italy, and Japan. The LSTM+ model incorporates two key optimizations: (1) fine-adjustment of parameters and (2) a ‘re-prediction’ process that utilizes the latest prediction results from the previous iteration. The performance of the LSTM+ model is evaluated and compared with that of Backpropagation (BP) and traditional LSTM models. The results demonstrate that the LSTM+ model significantly outperforms both BP and LSTM models, achieving a Mean Absolute Percentage Error (MAPE) of less than 0.6%. Additionally, two illustrative examples employing the LSTM+ model further validate its general applicability and practical performance for predicting cumulative confirmed COVID-19 cases.

Suggested Citation

  • M. He & W. W. Zhu & H. Z. Chen & Hongbing Zhu, 2024. "Application of optimized LSTM in prediction of the cumulative confirmed cases of COVID-19," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 27(13), pages 1893-1905, October.
    • Handle: RePEc:taf:gcmbxx:v:27:y:2024:i:13:p:1893-1905
    DOI: 10.1080/10255842.2023.2264438
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