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Using Geographically Weighted Regression (GWR) to Explore Spatial Varying Relationships of Immature Mosquitoes and Human Densities with the Incidence of Dengue

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  • Chia-Hsien Lin

    (Department of Geography, College of Science, National Taiwan University, Taipei 106, Taiwan)

  • Tzai-Hung Wen

    (Department of Geography, College of Science, National Taiwan University, Taipei 106, Taiwan)

Abstract

The only way for dengue to spread in the human population is through the human-mosquito-human cycle. Most research in this field discusses the dengue-mosquito or dengue-human relationships over a particular study area, but few have explored the local spatial variations of dengue-mosquito and dengue-human relationships within a study area. This study examined whether spatial heterogeneity exists in these relationships. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships and identify the geographical heterogeneities by using the information of entomology and dengue cases in the cities of Kaohsiung and Fengshan in 2002. Our findings indicate that dengue-mosquito and dengue-human relationships were significantly spatially non-stationary. This means that in some areas higher dengue incidences were associated with higher vector/host densities, but in some areas higher incidences were related to lower vector/host densities. We demonstrated that a GWR model can be used to geographically differentiate the relationships of dengue incidence with immature mosquito and human densities. This study provides more insights into spatial targeting of intervention and control programs against dengue outbreaks within the study areas.

Suggested Citation

  • Chia-Hsien Lin & Tzai-Hung Wen, 2011. "Using Geographically Weighted Regression (GWR) to Explore Spatial Varying Relationships of Immature Mosquitoes and Human Densities with the Incidence of Dengue," IJERPH, MDPI, vol. 8(7), pages 1-18, July.
    • Handle: RePEc:gam:jijerp:v:8:y:2011:i:7:p:2798-2815:d:13088
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    References listed on IDEAS

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    1. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    2. Derek A.T. Cummings & Rafael A. Irizarry & Norden E. Huang & Timothy P. Endy & Ananda Nisalak & Kumnuan Ungchusak & Donald S. Burke, 2004. "Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand," Nature, Nature, vol. 427(6972), pages 344-347, January.
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    Cited by:

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    2. Khalid Al-Ahmadi & Ali Al-Zahrani, 2013. "NO 2 and Cancer Incidence in Saudi Arabia," IJERPH, MDPI, vol. 10(11), pages 1-19, November.
    3. Mehmet Burak Kaya & Onur Alisan & Alican Karaer & Eren Erman Ozguven, 2024. "Assessing Tornado Impacts in the State of Kentucky with a Focus on Demographics and Roadways Using a GIS-Based Approach," Sustainability, MDPI, vol. 16(3), pages 1-27, January.
    4. Cyprian Chwiałkowski & Adam Zydroń & Dariusz Kayzer, 2022. "Assessing the Impact of Selected Attributes on Dwelling Prices Using Ordinary Least Squares Regression and Geographically Weighted Regression: A Case Study in Poznań, Poland," Land, MDPI, vol. 12(1), pages 1-20, December.

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