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How do temperature and precipitation drive dengue transmission in nine cities, in Guangdong Province, China: a Bayesian spatio-temporal model analysis

文献类型：期刊论文

作者Quan, Yi8; Zhang, Yingtao6,7; Deng, Hui6,7; Li, Xing8; Zhao, Jianguo8; Hu, Jianxiong8; Lu, Ruipeng6,7; Li, Yihan8; Zhang, Qian5,8; Zhang, Li8
刊名AIR QUALITY ATMOSPHERE AND HEALTH
出版日期2023-04-01
卷号N/A
关键词Dengue Climatic factors Bayesian analysis Spatio-temporal model
ISSN号1873-9326
DOI10.1007/s11869-023-01331-2
文献子类Article; Early Access
英文摘要Dengue remains an important public health issue in South China. In this study, we aim to quantify the effect of climatic factors on dengue in nine cities of the Pearl River Delta (PRD) in South China. Monthly dengue cases, climatic factors, socio-economic, geographical, and mosquito density data in nine cities of the PRD from 2008 to 2019 were collected. A generalized additive model (GAM) was applied to investigate the exposure-response relationship between climatic factors (temperature and precipitation) and dengue incidence in each city. A spatio-temporal conditional autoregressive model (ST-CAR) with a Bayesian framework was employed to estimate the effect of temperature and precipitation on dengue and to explore the temporal trend of the dengue risk by adjusting the socioeconomic and geographical factors. There was a positive non-linear association between the temperature and dengue incidence in the nine cities in south China, while the approximate linear negative relationship between precipitation and dengue incidence was found in most of the cities. The ST-CAR model analysis showed the risk of dengue transmission increased by 101.0% (RR: 2.010, 95% CI: 1.818 to 2.151) for 1 degrees C increase in monthly temperature at 2 months lag in the overall nine cities, while a 3.2% decrease (relative risk (RR): 0.968, 95% CI: 0.946 to 0.985) and a 2.1% decrease (RR: 0.979, 95% CI: 0.975 to 0.983) for 10 mm increase in monthly precipitation at present month and 3 months lag. The expected incidence of dengue has risen again since 2015, and the highest incidence was in Guangzhou City. Our study showed that climatic factors, including temperature and precipitation would drive the dengue transmission, and the dengue epidemic risk has been increasing. The findings may contribute to the climate-driven dengue prediction and dengue risk projection for future climate scenarios.
WOS关键词AEDES-AEGYPTI ; RISK ; CLIMATE ; FEVER ; VARIABILITY ; GUANGZHOU ; VECTOR
WOS研究方向Environmental Sciences & Ecology
WOS记录号WOS:000943650900003
出版者SPRINGER
源URL[http://ir.igsnrr.ac.cn/handle/311030/190249]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst L, Beijing, Peoples R China
2.Jinan Univ, Sch Med, Dept Publ Hlth & Prevent Med, Guangzhou 510632, Guangdong, Peoples R China
3.Sun Yat Sen Univ, Sch Publ Hlth, Guangzhou 510120, Guangdong, Peoples R China
4.Guangdong Pharmaceut Univ, Sch Publ Hlth, Guangzhou 510006, Peoples R China
5.Guangdong Workstn Emerging Infect Dis Control & Pr, Guangzhou 511430, Guangdong, Peoples R China
6.Guangdong Prov Ctr Dis Control & Prevent, Guangzhou 511430, Guangdong, Peoples R China
7.Guangdong Prov Ctr Dis Control & Prevent, Guangdong Prov Inst Publ Hlth, Guangzhou 511430, Guangdong, Peoples R China
8.Southern Med Univ, Sch Publ Hlth, Guangzhou 510515, Guangdong, Peoples R China
推荐引用方式
GB/T 7714		 Quan, Yi,Zhang, Yingtao,Deng, Hui,et al. How do temperature and precipitation drive dengue transmission in nine cities, in Guangdong Province, China: a Bayesian spatio-temporal model analysis[J]. AIR QUALITY ATMOSPHERE AND HEALTH,2023,N/A.
APA Quan, Yi.,Zhang, Yingtao.,Deng, Hui.,Li, Xing.,Zhao, Jianguo.,...&Xiao, Jianpeng.(2023).How do temperature and precipitation drive dengue transmission in nine cities, in Guangdong Province, China: a Bayesian spatio-temporal model analysis.AIR QUALITY ATMOSPHERE AND HEALTH,N/A.
MLA Quan, Yi,et al."How do temperature and precipitation drive dengue transmission in nine cities, in Guangdong Province, China: a Bayesian spatio-temporal model analysis".AIR QUALITY ATMOSPHERE AND HEALTH N/A(2023).

入库方式： OAI收割

来源：地理科学与资源研究所

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