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

ABSTRACT:

Background

The global incidence of Influenza-Like Illnesses (ILI) has demonstrated an overall increasing trend. In the context of climate change, it is imperative to conduct research on the impact of meteorological factors on epidemic prediction.

Objectives

To assess the potential of meteorological factors with Long Short-Term Memory (LSTM) models for improving ILI incidence prediction accuracy, providing a reference for the future development of related public health applicability.

Methods

Data on ILI incidence from November 2017 to January 2022, along with corresponding meteorological data over the same period. Pearson correlation analysis was employed to validate the relationship between the meteorological data and ILI incidence. Various LSTM architectures to forec

SUBMITTER: Li PL 

PROVIDER: S-EPMC12357460 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature