Integrating meteorological data and hybrid intelligent models for dengue fever prediction

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.

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 dengue warning levels using a fuzzy clustering algorithm. The improved horned lizard optimization algorithm (IHLOA) was then combined with support vector machine (SVM), random forest (RF) and k-nearest neighbor (KNN) for dengue prediction. The average accuracy ( $$ ), average fitness value ( $$ ), average feature reduction rate ( $$ ), standard deviation (STD) and $$ were used to evaluate prediction performance.

The incidence risk of dengue fever was positively correlated with temperature, relative humidity, sunshine duration and the vegetation index but negatively correlated with visibility, wind speed and sea level pressure. Meteorological factors had a lag effect on the risk of dengue fever, and the magnitude of the effect varies dynamically with lag time. Compared with the other prediction models, our proposed hybrid prediction models exhibited relatively low $$ values and relatively high $$ values, indicating the best prediction results.

Our experiment revealed the correlation and lag effect between meteorological factors and the incidence of dengue fever, indicating that meteorological factors have important value in predicting dengue fever. In addition, the hybrid prediction models constructed in this article can accurately predict outbreaks of dengue fever, which can lay a theoretical foundation for the construction of monitoring and early warning systems and improve the ability of relevant government departments to detect and identify dengue fever outbreaks in a timely manner.