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

Chi-Chieh Huang, Tuen Yee Tiffany Tam, Yinq-Rong Chern, Shih-Chun Candice Lung, Nai-Tzu Chen and Chih-Da Wu
Additional contact information
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
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

IJERPH, 2018, vol. 15, issue 9, 1-12

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.

Keywords: dengue fever; surrounding greenness; spatial clustering; GLMM (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1660-4601/15/9/1869/pdf (application/pdf)
https://www.mdpi.com/1660-4601/15/9/1869/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:15:y:2018:i:9:p:1869-:d:166446

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().