IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v14y2017i12p1518-d121830.html
   My bibliography  Save this article

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

Author

Listed:
  • Hongyan Ren

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Lan Zheng

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China)

  • Qiaoxuan Li

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Geographical Science, Fujian Normal University, Fuzhou 350007, China)

  • Wu Yuan

    (School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China)

  • Liang Lu

    (Department of Vector Biology and Control, Natural Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China)

Abstract

Dengue fever (DF) is a common and rapidly spreading vector-borne viral disease in tropical and subtropical regions. In recent years, this imported disease has posed an increasing threat to public health in China, especially in many southern cities. Although the severity of DF outbreaks in these cities is generally associated with known risk factors at various administrative levels, spatial heterogeneities of these associations remain little understood on a finer scale. In this study, the neighboring Guangzhou and Foshan (GF) cities were considered as a joint area for characterizing the spatial variations in the 2014 DF epidemic at various grid levels from 1 × 1 km 2 to 6 × 6 km 2 . On an appropriate scale, geographically weighted regression (GWR) models were employed to interpret the influences of socioeconomic and environmental factors on this epidemic across the GF area. DF transmissions in Guangzhou and Foshan cities presented synchronous temporal changes and spatial expansions during the main epidemic months. Across the GF area, this epidemic was obviously spatially featured at various grid levels, especially on the 2 × 2 km 2 scale. Its spatial variations were relatively sufficiently explained by population size, road density, and economic status integrated in the GWR model with the lowest Akaike Information Criterion (AICc = 5227.97) and highest adjusted R square (0.732) values. These results indicated that these three socioeconomic factors acted as geographical determinants of spatial variability of the 2014 DF epidemic across the joint GF area, although some other potential factors should be added to improve the explaining the spatial variations in the central zones. This work improves our understanding of the effects of socioeconomic conditions on the spatial variations in this epidemic and helps local hygienic authorities to make targeted joint interventions for preventing and controlling this epidemic across the GF area.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:12:p:1518-:d:121830
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/14/12/1518/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/14/12/1518/
    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. Shaowei Sang & Wenwu Yin & Peng Bi & Honglong Zhang & Chenggang Wang & Xiaobo Liu & Bin Chen & Weizhong Yang & Qiyong Liu, 2014. "Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    3. Anselin, Luc & Getis, Arthur, 1992. "Spatial Statistical Analysis and Geographic Information Systems," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 26(1), pages 19-33, April.
    4. Qiaoxuan Li & Hongyan Ren & Lan Zheng & Wei Cao & An Zhang & Dafang Zhuang & Liang Lu & Huixian Jiang, 2017. "Ecological Niche Modeling Identifies Fine-Scale Areas at High Risk of Dengue Fever in the Pearl River Delta, China," IJERPH, MDPI, vol. 14(6), pages 1-13, June.
    5. Song, Yan & Zenou, Yves, 2012. "Urban villages and housing values in China," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 495-505.
    6. 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)

    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. Shi Yin & Chao Ren & Yuan Shi & Junyi Hua & Hsiang-Yu Yuan & Lin-Wei Tian, 2022. "A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings," IJERPH, MDPI, vol. 19(22), pages 1-20, November.
    2. Yujuan Yue & Qiyong Liu, 2019. "Exploring Epidemiological Characteristics of Domestic Imported Dengue Fever in Mainland China, 2014–2018," IJERPH, MDPI, vol. 16(20), pages 1-10, October.
    3. 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.
    4. Yujuan Yue & Xiaobo Liu & Dongsheng Ren & Haixia Wu & Qiyong Liu, 2021. "Spatial Dynamics of Dengue Fever in Mainland China, 2019," IJERPH, MDPI, vol. 18(6), pages 1-12, March.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Yingtao Zhang & Tao Wang & Kangkang Liu & Yao Xia & Yi Lu & Qinlong Jing & Zhicong Yang & Wenbiao Hu & Jiahai Lu, 2016. "Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 10(2), pages 1-17, February.
    10. Shaowei Sang & Shaohua Gu & Peng Bi & Weizhong Yang & Zhicong Yang & Lei Xu & Jun Yang & Xiaobo Liu & Tong Jiang & Haixia Wu & Cordia Chu & Qiyong Liu, 2015. "Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(5), pages 1-12, May.
    11. 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.
    12. 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.
    13. Yingjie Zhang & Siqi Zheng & Yan Song & Yongguang Zhong, 2016. "The Spillover Effect of Urban Village Removal on Nearby Home Values in Beijing," Growth and Change, Wiley Blackwell, vol. 47(1), pages 9-31, March.
    14. Ye, Xinyue & Yue, Wenze, 2014. "Comparative analysis of regional development: Exploratory space-time data analysis and open source implementation," Economics Discussion Papers 2014-20, Kiel Institute for the World Economy (IfW Kiel).
    15. 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.
    16. 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.
    17. Jiang, Dong & Wang, Qian & Ding, Fangyu & Fu, Jingying & Hao, Mengmeng, 2019. "Potential marginal land resources of cassava worldwide: A data-driven analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 167-173.
    18. Benjamin Lopez-Jimena & Michaël Bekaert & Mohammed Bakheit & Sieghard Frischmann & Pranav Patel & Etienne Simon-Loriere & Louis Lambrechts & Veasna Duong & Philippe Dussart & Graham Harold & Cheikh Fa, 2018. "Development and validation of four one-step real-time RT-LAMP assays for specific detection of each dengue virus serotype," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 12(5), pages 1-22, May.
    19. Shuyang Li & Fei Qu, 2022. "Preserving Authenticity in Urban Regeneration: A Framework for the New Definition from the Perspective of Multi-Subject Stakeholders—A Case Study of Nantou in Shenzhen, China," IJERPH, MDPI, vol. 19(15), pages 1-16, July.
    20. Fazli Wahid & Dr.Sajjad Ali & Jan Muhammad, 2021. "Effective Sources of Information in Winter Seasonal Diseases: The Perception of Residents of District Buner, KP," Journal of Media & Communication (JMC), Ilma University, Faculty of Media & Design, vol. 1(2), pages 215-229.

    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:14:y:2017:i:12:p:1518-:d:121830. 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.