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

M He; W W Zhu; H Z Chen; Hongbing Zhu

doi:10.1080/10255842.2023.2264438

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

Comput Methods Biomech Biomed Engin. 2023 Oct 3:1-13. doi: 10.1080/10255842.2023.2264438. Online ahead of print.

Authors

M He¹, W W Zhu², H Z Chen², Hongbing Zhu³

Affiliations

¹ College of Electronics and Information Engineering, Beibu Gulf University, Qinzhou, China.
² College of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou, China.
³ Advanced Science and Technology Research Institute, Beibu Gulf University, Qinzhou, China.

PMID: 37787059
DOI: 10.1080/10255842.2023.2264438

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.

Keywords: Covid-19; LSTM; cumulative confirmed cases; prediction.