The Impact of Extreme Weather on Dengue Fever in Guangzhou, China: A Zero-Inflated Negative Binomial Spatial Lag Analysis.

ABSTRACT: Climate change intensifies extreme weather, which in turn influences infectious disease transmission. As a dengue fever (DF) hotspot, Guangzhou lacks research on how extreme weather characteristics and spatial factors interact to shape DF patterns. This study analyzed DF distribution in Guangzhou from 2017 to 2019, using a zero-inflated negative binomial spatial lag (ZINB-SAR) regression model to assess the effects of daytime heatwaves (DHW), nighttime heatwaves (NHW) and extreme precipitation (EP) on DF. Results revealed that DF cases were predominantly clustered in central urban areas, with an epidemic season from May to November. The ZINB-SAR model outperformed negative binomial regression and spatial econometric models, with all spatial effect coefficients significantly positive. Analy

SUBMITTER: Ouyang X 

PROVIDER: S-EPMC12485294 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature