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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.

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 km² to 6 × 6 km². 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 km² 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.

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  • Authors+Show Affiliations

    ,

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. renhy@igsnrr.ac.cn.

    ,

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. zhenglan1007@163.com. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China. zhenglan1007@163.com.

    ,

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. liqx@lreis.ac.cn. College of Geographical Science, Fujian Normal University, Fuzhou 350007, China. liqx@lreis.ac.cn.

    ,

    School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China. yuanwu@bit.edu.cn.

    Department of Vector Biology and Control, Natural Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China. luliang@icdc.cn.

    Source

    Pub Type(s)

    Journal Article

    Language

    eng

    PubMed ID

    29211001