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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

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Abstract

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 °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.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

PRD :

Pearl River Delta

GAM :

Generalized additive model

CAR :

Conditional autoregressive

NNIDRIS :

National Notifiable Infectious Diseases Reporting Information System

MOI :

Mosquito oviposition index

GDCDC :

Guangdong Provincial Center for Disease Control and Prevention

NDVI :

Normalized difference vegetation index

MCMC :

Markov chain Monte Carlo

RR :

Relative risk

ER :

Excess risk

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Funding

This work was supported by the National Key Research and Development Program of China (2018YFA0606200), the Natural Science Foundation of China (42071377), and the Key-Area Research and Development Program of Guangdong Province (2019B111103001).

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Author notes

  1. Yi Quan, Yingtao Zhang and Hui Deng contributed equally to this work.

Authors and Affiliations

  1. School of Public Health, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Yi Quan, Yihan Li, Li Zhang, Jiong Wang & Jianpeng Xiao

  2. Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, Guangdong, China

    Yi Quan, Xing Li, Jianguo Zhao, Jianxiong Hu, Yihan Li, Qian Zhang, Li Zhang, Zitong Huang, Jiong Wang & Jianpeng Xiao

  3. Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, Guangdong, China

    Yingtao Zhang, Hui Deng, Ruipeng Lu, Aiping Deng, Liping Liu & Lifeng Lin

  4. Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Guangzhou, 511430, Guangdong, China

    Yingtao Zhang, Hui Deng, Ruipeng Lu, Aiping Deng, Liping Liu & Lifeng Lin

  5. School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510006, China

    Qian Zhang

  6. School of Public Health, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China

    Zitong Huang

  7. Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, 510632, Guangdong, China

    Tao Liu & Wenjun Ma

  8. State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Zhoupeng Ren

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  1. Yi Quan
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Contributions

Y. Quan and J. Xiao wrote the manuscript, and J. Xiao, Z. Ren, X. Li, and L. Lin conceived and designed the study and reviewed and revised the manuscript. Y. Zhang, H. Deng, A. Deng, R. Lu, Y. Quan, J. Hu, J. Zhao, T. Liu, and W. Ma contributed to data collection and statistical analysis. Y. Li, Q. Zhang, L. Zhang, J. Wang, and Z. Huang contributed to data visualization. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Lifeng Lin, Zhoupeng Ren or Jianpeng Xiao.

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The study was approved by the ethics committee of the Guangdong Provincial Center for Disease Control and Prevention (No. W96-027E-201925).

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The authors declare no competing interests.

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Quan, Y., Zhang, Y., Deng, H. et al. How do temperature and precipitation drive dengue transmission in nine cities, in Guangdong Province, China: a Bayesian spatio-temporal model analysis. Air Qual Atmos Health 16, 1153–1163 (2023). https://doi.org/10.1007/s11869-023-01331-2

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