Prediction of influenza-like illness incidence using meteorological factors in Kunming
Deep learning model study
Bibliographic Data
| ID | 15367409 |
|---|---|
| Authors | Peilong Li (0000-0002-7441-6514, corresponding author), Pei-long Li, Rongwei Huang (0009-0002-2458-7777), Rong-wei Huang, Ruiyu Xie (0000-0003-0518-2897, Affiliated Hospital of Youjiang Medical University for Nationalities), Rong-man Xie, Yanqing Sun (0000-0002-3022-369X), Juan Xie (0000-0002-9127-7931), Kai Liu (0000-0002-7995-6456) |
| Year | 2025 |
| Volume | 25 |
| Issue | 1 |
| Pages | 2796-2796 |
| Publication date | 2025-08-16 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMC Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Publisher | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-025-23710-3 |
| PMID | 40818971 |
| OpenAlex | W4413270841 |
| Language | EN |
| Citations received | 1 |
| References cited | 26 |
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
Context (archaeology · Geography · Incidence (geometry · Machine learning · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Influenza Virus Research Studies · Mathematics
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |