Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study

The study demonstrates that stacking layers within LSTM models and incorporating KAN can further enhance the representational capabilities of these models. These improvements suggest that by leveraging meteorological data and utilizing advanced LSTM architectures, those can achieve more accurate and reliable predictions of ILI incidence.

Authors

Ruiyu Xie (ORCID: https://orcid.org/0000-0003-0518-2897)
Peilong Li (ORCID: https://orcid.org/0000-0002-7441-6514)
Kai Liu (ORCID: https://orcid.org/0000-0002-3374-3375)
Rongwei Huang
Juan Xie (ORCID: https://orcid.org/0000-0002-9127-7931)

Institutions

Affiliated Hospital of Youjiang Medical University for Nationalities (CN)

Publication Details

Journal: BMC Public Health
Published: 2025-08-16
DOI: https://doi.org/10.1186/s12889-025-23710-3
Primary Topic: Influenza Virus Research Studies
Type: article
Field-Weighted Citation Impact: 0.00

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article

Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study

Ruiyu Xie, Peilong Li, Kai Liu, Rongwei Huang et al.

Aug 16, 2025

BMC Public Health

Influenza Virus Research Studies

article

Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study

Ruiyu Xie, Peilong Li, Kai Liu, Rongwei Huang, Juan Xie

August 16, 2025article en

Abstract

The study demonstrates that stacking layers within LSTM models and incorporating KAN can further enhance the representational capabilities of these models. These improvements suggest that by leveraging meteorological data and utilizing advanced LSTM architectures, those can achieve more accurate and reliable predictions of ILI incidence.

BMC Public HealthVol. 25(1)

Affiliated Hospital of Youjiang Medical University for Nationalities (CN)

Good health and well-being

Openalex Percentile: Top 71%

Influenza Virus Research Studies

DOI Open Access PDF OpenAlex

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