IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i9p867-d77118.html
   My bibliography  Save this article

Meteorological Factors for Dengue Fever Control and Prevention in South China

Author

Listed:
  • Haogao Gu

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Health Information Research Center, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Sun Yat-sen Global Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Ross Ka-Kit Leung

    (Division of Public Health Laboratory Sciences, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
    Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China)

  • Qinlong Jing

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Health Information Research Center, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Sun Yat-sen Global Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Wangjian Zhang

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Health Information Research Center, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Sun Yat-sen Global Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Zhicong Yang

    (Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China)

  • Jiahai Lu

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Health Information Research Center, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Sun Yat-sen Global Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Yuantao Hao

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Health Information Research Center, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Sun Yat-sen Global Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Dingmei Zhang

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Health Information Research Center, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Guangdong Key Laboratory of Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
    Sun Yat-sen Global Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

Abstract

Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005–2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

Suggested Citation

  • Haogao Gu & Ross Ka-Kit Leung & Qinlong Jing & Wangjian Zhang & Zhicong Yang & Jiahai Lu & Yuantao Hao & Dingmei Zhang, 2016. "Meteorological Factors for Dengue Fever Control and Prevention in South China," IJERPH, MDPI, vol. 13(9), pages 1-12, August.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:9:p:867-:d:77118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/9/867/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/13/9/867/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Samir Bhatt & Peter W. Gething & Oliver J. Brady & Jane P. Messina & Andrew W. Farlow & Catherine L. Moyes & John M. Drake & John S. Brownstein & Anne G. Hoen & Osman Sankoh & Monica F. Myers & Dylan , 2013. "The global distribution and burden of dengue," Nature, Nature, vol. 496(7446), pages 504-507, April.
    2. Hai-Yan Xu & Xiuju Fu & Lionel Kim Hock Lee & Stefan Ma & Kee Tai Goh & Jiancheng Wong & Mohamed Salahuddin Habibullah & Gary Kee Khoon Lee & Tian Kuay Lim & Paul Anantharajah Tambyah & Chin Leong Lim, 2014. "Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 8(5), pages 1-11, May.
    3. Xiaopeng Qi & Yong Wang & Yue Li & Yujie Meng & Qianqian Chen & Jiaqi Ma & George F Gao, 2015. "The Effects of Socioeconomic and Environmental Factors on the Incidence of Dengue Fever in the Pearl River Delta, China, 2013," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(10), pages 1-13, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jundi Liu & Xiaolu Tian & Yu Deng & Zhicheng Du & Tianzhu Liang & Yuantao Hao & Dingmei Zhang, 2019. "Risk Factors Associated with Dengue Virus Infection in Guangdong Province: A Community-Based Case-Control Study," IJERPH, MDPI, vol. 16(4), pages 1-12, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Renaud Marti & Zhichao Li & Thibault Catry & Emmanuel Roux & Morgan Mangeas & Pascal Handschumacher & Jean Gaudart & Annelise Tran & Laurent Demagistri & Jean-François Faure & José Joaquín Carvajal & , 2020. "A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires," Post-Print hal-02682042, HAL.
    2. Hongyan Ren & Lan Zheng & Qiaoxuan Li & Wu Yuan & Liang Lu, 2017. "Exploring Determinants of Spatial Variations in the Dengue Fever Epidemic Using Geographically Weighted Regression Model: A Case Study in the Joint Guangzhou-Foshan Area, China, 2014," IJERPH, MDPI, vol. 14(12), pages 1-13, December.
    3. Chi-Chieh Huang & Tuen Yee Tiffany Tam & Yinq-Rong Chern & Shih-Chun Candice Lung & Nai-Tzu Chen & Chih-Da Wu, 2018. "Spatial Clustering of Dengue Fever Incidence and Its Association with Surrounding Greenness," IJERPH, MDPI, vol. 15(9), pages 1-12, August.
    4. Shuli Zhou & Suhong Zhou & Lin Liu & Meng Zhang & Min Kang & Jianpeng Xiao & Tie Song, 2019. "Examining the Effect of the Environment and Commuting Flow from/to Epidemic Areas on the Spread of Dengue Fever," IJERPH, MDPI, vol. 16(24), pages 1-13, December.
    5. Jue Tao Lim & Yiting Han & Borame Sue Lee Dickens & Lee Ching Ng & Alex R Cook, 2020. "Time varying methods to infer extremes in dengue transmission dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.
    6. Yebin Chen & Zhigang Zhao & Zhichao Li & Weihong Li & Zhipeng Li & Renzhong Guo & Zhilu Yuan, 2019. "Spatiotemporal Transmission Patterns and Determinants of Dengue Fever: A Case Study of Guangzhou, China," IJERPH, MDPI, vol. 16(14), pages 1-14, July.
    7. Ting-Wu Chuang & Ka-Chon Ng & Thi Luong Nguyen & Luis Fernando Chaves, 2018. "Epidemiological Characteristics and Space-Time Analysis of the 2015 Dengue Outbreak in the Metropolitan Region of Tainan City, Taiwan," IJERPH, MDPI, vol. 15(3), pages 1-12, February.
    8. Yu-Chieh Cheng & Fang-Jing Lee & Ya-Ting Hsu & Eric V Slud & Chao A Hsiung & Chun-Hong Chen & Ching-Len Liao & Tzai-Hung Wen & Chiu-Wen Chang & Jui-Hun Chang & Hsiao-Yu Wu & Te-Pin Chang & Pei-Sheng L, 2020. "Real-time dengue forecast for outbreak alerts in Southern Taiwan," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(7), pages 1-18, July.
    9. Felipe J. Colón-González & Rory Gibb & Kamran Khan & Alexander Watts & Rachel Lowe & Oliver J. Brady, 2023. "Projecting the future incidence and burden of dengue in Southeast Asia," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    10. Jiucheng Xu & Keqiang Xu & Zhichao Li & Fengxia Meng & Taotian Tu & Lei Xu & Qiyong Liu, 2020. "Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
    11. Ting-Wu Chuang & Luis Fernando Chaves & Po-Jiang Chen, 2017. "Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-20, June.
    12. Sakirul Khan & Sheikh Mohammad Fazle Akbar & Takaaki Yahiro & Mamun Al Mahtab & Kazunori Kimitsuki & Takehiro Hashimoto & Akira Nishizono, 2022. "Dengue Infections during COVID-19 Period: Reflection of Reality or Elusive Data Due to Effect of Pandemic," IJERPH, MDPI, vol. 19(17), pages 1-12, August.
    13. Shengzhang Dong & George Dimopoulos, 2023. "Aedes aegypti Argonaute 2 controls arbovirus infection and host mortality," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    14. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    15. Eunha Shim, 2017. "Cost-effectiveness of dengue vaccination in Yucatán, Mexico using a dynamic dengue transmission model," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-17, April.
    16. Dominik Kiemel & Ann-Sophie Helene Kroell & Solène Denolly & Uta Haselmann & Jean-François Bonfanti & Jose Ignacio Andres & Brahma Ghosh & Peggy Geluykens & Suzanne J. F. Kaptein & Lucas Wilken & Piet, 2024. "Pan-serotype dengue virus inhibitor JNJ-A07 targets NS4A-2K-NS4B interaction with NS2B/NS3 and blocks replication organelle formation," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    17. Hone-Jay Chu & Bo-Cheng Lin & Ming-Run Yu & Ta-Chien Chan, 2016. "Minimizing Spatial Variability of Healthcare Spatial Accessibility—The Case of a Dengue Fever Outbreak," IJERPH, MDPI, vol. 13(12), pages 1-11, December.
    18. Cheng-Te Lin & Yu-Sheng Huang & Lu-Wen Liao & Chung-Te Ting, 2020. "Measuring Consumer Willingness to Pay to Reduce Health Risks of Contracting Dengue Fever," IJERPH, MDPI, vol. 17(5), pages 1-15, March.
    19. Amy R. Krystosik & Andrew Curtis & A. Desiree LaBeaud & Diana M. Dávalos & Robinson Pacheco & Paola Buritica & Álvaro A. Álvarez & Madhav P. Bhatta & Jorge Humberto Rojas Palacios & Mark A. James, 2018. "Neighborhood Violence Impacts Disease Control and Surveillance: Case Study of Cali, Colombia from 2014 to 2016," IJERPH, MDPI, vol. 15(10), pages 1-20, September.
    20. Laith Hussain-Alkhateeb & Tatiana Rivera Ramírez & Axel Kroeger & Ernesto Gozzer & Silvia Runge-Ranzinger, 2021. "Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 15(9), pages 1-25, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:13:y:2016:i:9:p:867-:d:77118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.