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Influences of Differentiated Residence and Workplace Location on the Identification of Spatiotemporal Patterns of Dengue Epidemics: A Case Study in Guangzhou, China

Author

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  • Yuqi Zhang

    (State Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China
    Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China)

  • Hongyan Ren

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Runhe Shi

    (State Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China
    Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China
    Joint Laboratory for Environmental Remote Sensing and Data Assimilation, ECNU&CEODE Ministry of Education, East China Normal University, Shanghai 200241, China)

Abstract

The location of the infections is the basic data for precise prevention and control of dengue fever (DF). However, most studies default to residence address as the place of infection, ignoring the possibility that cases are infected at other places (e.g., workplace address). This study aimed to explore the spatiotemporal patterns of DF in Guangzhou from 2016 to 2018, differentiating workplace and residence. In terms of temporal and spatial dimensions, a case weight assignment method that differentiates workplace and residence location was proposed, taking into account the onset of cases around their workplace and residence. Logistic modeling was used to classify the epidemic phases. Spatial autocorrelation analysis was used to reveal the high and early incidence areas of DF in Guangzhou from 2016 to 2018. At high temporal resolution, the DF in Guangzhou has apparent phase characteristics and is consistent with logistic growth. The local epidemic is clustered in terms of the number of cases and the time of onset and outbreak. High and early epidemic areas are mainly distributed in the central urban areas of Baiyun, Yuexiu, Liwan and Haizhu districts. The high epidemic areas due to commuting cases can be further identified after considering the workplaces of cases. Improving the temporal resolution and differentiating the workplace and residence address of cases could help to improve the identification of early and high epidemic areas in analyzing the spatiotemporal patterns of dengue fever in Guangzhou, which could more reasonably reflect the spatiotemporal patterns of DF in the study area.

Suggested Citation

  • Yuqi Zhang & Hongyan Ren & Runhe Shi, 2022. "Influences of Differentiated Residence and Workplace Location on the Identification of Spatiotemporal Patterns of Dengue Epidemics: A Case Study in Guangzhou, China," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13393-:d:944521
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    References listed on IDEAS

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