Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis
Abstract 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 de...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00218-9 |
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| author | Xingyuan Yu Xia Wang Sanyi Tang |
| author_facet | Xingyuan Yu Xia Wang Sanyi Tang |
| author_sort | Xingyuan Yu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-7a34b069b5fc4dd89840e061a5c7c93f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7a34b069b5fc4dd89840e061a5c7c93f2025-08-20T02:55:21ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-00218-9Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysisXingyuan Yu0Xia Wang1Sanyi Tang2School of Mathematics and Statistics, Shaanxi Normal UniversitySchool of Mathematics and Statistics, Shaanxi Normal UniversitySchool of Mathematical Sciences, Shanxi UniversityAbstract 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.https://doi.org/10.1038/s41598-025-00218-9DengueConvergent cross mappingPartial cross mappingGeneralized additive modelLagDistributed lag nonlinear model |
| spellingShingle | Xingyuan Yu Xia Wang Sanyi Tang Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis Scientific Reports Dengue Convergent cross mapping Partial cross mapping Generalized additive model Lag Distributed lag nonlinear model |
| title | Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis |
| title_full | Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis |
| title_fullStr | Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis |
| title_full_unstemmed | Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis |
| title_short | Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis |
| title_sort | assessing the influencing factors of dengue fever in chinese mainland based on causal analysis |
| topic | Dengue Convergent cross mapping Partial cross mapping Generalized additive model Lag Distributed lag nonlinear model |
| url | https://doi.org/10.1038/s41598-025-00218-9 |
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