Nonlinear and potential driving impacts of meteorological and air pollution factors on influenza-like illness in Jinan, China.

ABSTRACT:

Background

While many studies have explored the correlation between environmental factors and influenza, research on their potential causal associations remains limited. Further, the impact of temperature changes between neighboring days (TCN) has not been thoroughly investigated.

Methods

Influenza-like illness (ILI) data, meteorological indicators, and air pollutant levels were collected in Jinan, China (2015-2019). Gradient boosting decision trees (GBDT) were used to identify key environmental variables. The distributed lag nonlinear models (DLNM) and empirical dynamic modeling (EDM) framework were then applied to explore their nonlinear associations with and potential causal effects on influenza infection. Subgroup analysis was also performed by different age groups.

R

SUBMITTER: Zuo H 

PROVIDER: S-EPMC12781324 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature