Integrating meteorological data and hybrid intelligent models for dengue fever prediction.

ABSTRACT:

Background

Dengue fever is a globally prevalent arbovirus disease that poses a serious challenge to global health. Therefore, analyzing the relationship between dengue fever incidence and meteorological factors and developing a more effective prediction model based on this relationship can provide a theoretical basis for public health departments to formulate reasonable prevention strategies.

Methods

We collected dengue fever cases and meteorological data, including temperature, humidity, sunshine duration, etc., from Guangdong and Zhejiang Provinces in China from 2005-2024. A distributed lag nonlinear model (DLNM) was used to analyze the exposure-response relationship between meteorological factors and dengue incidence. Moreover, the raw case data were classified into dengu

SUBMITTER: Cheng Y 

PROVIDER: S-EPMC12020127 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature