Yu Xingyuan, Wang Xia, Tang Sanyi
School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
School of Mathematical Sciences, Shanxi University, Taiyuan, 030006, People's Republic of China.
Sci Rep. 2025 May 1;15(1):15311. doi: 10.1038/s41598-025-00218-9.
Previous studies have identified various factors affecting dengue fever, but most focus on correlations within specific regions, not establishing causality. This study uses Convergent Cross Mapping (CCM) to explore the causal relationships between nine meteorological factors and reported dengue fever cases in 14 Chinese provinces with the highest incidence. Results show that temperature and pressure have causal links with case numbers in more provinces. In Guangdong, which has the most reported cases, Partial Cross Mapping (PCM) reveals a direct causal relationship only between GDP and reported dengue fever cases, while meteorological factors influence dengue fever via their impact on mosquito populations. Principal Component Analysis (PCA) from 30 provinces further confirms the importance of temperature and pressure. Given the significant negative correlation between temperature and pressure, separate models were developed for each province using the Distributed Lag Nonlinear Model (DLNM) combined with the Generalized Additive Model (GAM), with GDP as a covariate. The results indicate that the Relative Risk (RR) increases significantly under high temperatures and low pressure within a shorter lag period. GDP significantly promotes case numbers in all provinces.
以往的研究已经确定了影响登革热的各种因素，但大多数研究集中在特定区域内的相关性，并未确立因果关系。本研究使用收敛交叉映射（CCM）方法，探讨了9种气象因素与中国登革热发病率最高的14个省份报告的登革热病例之间的因果关系。结果表明，温度和气压与更多省份的病例数存在因果联系。在报告病例数最多的广东省，部分交叉映射（PCM）显示仅GDP与报告的登革热病例之间存在直接因果关系，而气象因素通过对蚊子种群的影响来影响登革热。对30个省份进行的主成分分析（PCA）进一步证实了温度和气压的重要性。鉴于温度和气压之间存在显著的负相关关系，以GDP作为协变量，使用分布滞后非线性模型（DLNM）结合广义相加模型（GAM）为每个省份建立了单独的模型。结果表明，在较短的滞后时间内，高温和低压条件下相对风险（RR）显著增加。GDP在所有省份都显著促进了病例数的增加。
