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Spatial Clustering of Dengue Fever Incidence and Its Association with Surrounding Greenness

Author

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  • Chi-Chieh Huang

    (Department of Forestry and Natural Resources, National Chiayi University, Chiayi 60004, Taiwan)

  • Tuen Yee Tiffany Tam

    (Department of Forestry and Natural Resources, National Chiayi University, Chiayi 60004, Taiwan)

  • Yinq-Rong Chern

    (Department of Forestry and Natural Resources, National Chiayi University, Chiayi 60004, Taiwan)

  • Shih-Chun Candice Lung

    (Research Center for Environmental Changes, Academia Sinica, Taipei 11529, Taiwan
    Department of Atmospheric Sciences, National Taiwan University, Taipei 10617, Taiwan
    Institute of Environmental Health, National Taiwan University, Taipei 10055, Taiwan)

  • Nai-Tzu Chen

    (National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan)

  • Chih-Da Wu

    (Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan)

Abstract

With more than 58,000 cases reported by the country’s Centers for Disease Control, the dengue outbreaks from 2014 to 2015 seriously impacted the southern part of Taiwan. This study aims to assess the spatial autocorrelation of the dengue fever (DF) outbreak in southern Taiwan in 2014 and 2015, and to further understand the effects of green space (such as forests, farms, grass, and parks) allocation on DF. In this study, two different greenness indexes were used. The first green metric, the normalized difference vegetation index (NDVI), was provided by the long-term NASA MODIS satellite NDVI database, which quantifies and represents the overall vegetation greenness. The latest 2013 land use survey GIS database completed by the National Land Surveying and Mapping Center was obtained to access another green metric, green land use in Taiwan. We first used Spearman’s rho to find out the relationship between DF and green space, and then three spatial autocorrelation methods, including Global Moran’s I, high/low clustering, and Hot Spot were employed to assess the spatial autocorrelation of DF outbreak. In considering the impact of social and environmental factors in DF, we used generalized linear mixed models (GLMM) to further clarify the relationship between different types of green land use and dengue cases. Results of spatial autocorrelation analysis showed a high aggregation of dengue epidemic in southern Taiwan, and the metropolitan areas were the main hotspots. Results of correlation analysis and GLMM showed a positive correlation between parks and dengue fever, and the other five green space metrics and land types revealed a negative association with DF. Our findings may be an important asset for improving surveillance and control interventions for dengue.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:1869-:d:166446
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    2. 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.
    3. Zhichao Li & Helen Gurgel & Nadine Dessay & Luojia Hu & Lei Xu & Peng Gong, 2020. "Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation," IJERPH, MDPI, vol. 17(12), pages 1-29, June.
    4. Kun Wang & Zhihao Sun & Meng Cai & Lingbo Liu & Hao Wu & Zhenghong Peng, 2022. "Impacts of Urban Blue-Green Space on Residents’ Health: A Bibliometric Review," IJERPH, MDPI, vol. 19(23), pages 1-21, December.

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