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Extreme weather events and dengue outbreaks in Guangzhou, China: a time-series quasi-binomial distributed lag non-linear model

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Abstract

Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006–2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ≥2 days with temperature ≥ 95th percentile, and extreme rainfall and humidity defined as daily values ≥95th percentile during 2006–2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115–251% around 6 weeks after heatwaves, 173–258% around 6–13 weeks after extremely high rainfall, and 572–587% around 6–13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model’s sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.

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Acknowledgments

The authors would like to thank China Centre for Disease Control and Prevention and National Meteorological Information Center for providing data set for this study.

Funding

This study was funded by the National Health and Medical Research Council (NHMRC) of Australia (project grant: APP 1138622).

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Authors and Affiliations

  1. School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, 4059, Australia

    Jian Cheng, Hilary Bambrick, Zhiwei Xu & Wenbiao Hu

  2. Department of Epidemiology and Biostatistics & Anhui Province Key Laboratory of Major Autoimmune Disease, School of Public Health, Anhui Medical University, Anhui, China

    Jian Cheng

  3. Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia

    Francesca D. Frentiu

  4. Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia

    Gregor Devine

  5. Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK

    Laith Yakob

  6. School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Australia

    Zhiwei Xu

  7. Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China

    Zhongjie Li & Weizhong Yang

  8. School of Population Medicine & Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China

    Weizhong Yang

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  1. Jian Cheng
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Correspondence to Wenbiao Hu.

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Cheng, J., Bambrick, H., Frentiu, F.D. et al. Extreme weather events and dengue outbreaks in Guangzhou, China: a time-series quasi-binomial distributed lag non-linear model. Int J Biometeorol 65, 1033–1042 (2021). https://doi.org/10.1007/s00484-021-02085-1

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