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

M. He, W. W. Zhu, H. Z. Chen and Hongbing Zhu

Computer Methods in Biomechanics and Biomedical Engineering, 2024, vol. 27, issue 13, 1893-1905

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.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10255842.2023.2264438 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gcmbxx:v:27:y:2024:i:13:p:1893-1905

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gcmb20

DOI: 10.1080/10255842.2023.2264438

Access Statistics for this article

Computer Methods in Biomechanics and Biomedical Engineering is currently edited by Director of Biomaterials John Middleton

More articles in Computer Methods in Biomechanics and Biomedical Engineering from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().