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Effect of Climatic Factors and Population Density on the Distribution of Dengue in Sri Lanka: A GIS Based Evaluation for Prediction of Outbreaks

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  • PDNN Sirisena
  • Faseeha Noordeen
  • Harithra Kurukulasuriya
  • Thanuja ALAR Romesh
  • LakKumar Fernando

Abstract

Dengue is one of the major hurdles to the public health in Sri Lanka, causing high morbidity and mortality. The present study focuses on the use of geographical information systems (GIS) to map and evaluate the spatial and temporal distribution of dengue in Sri Lanka from 2009 to 2014 and to elucidate the association of climatic factors with dengue incidence. Epidemiological, population and meteorological data were collected from the Epidemiology Unit, Department of Census and Statistics and the Department of Meteorology of Sri Lanka. Data were analyzed using SPSS (Version 20, 2011) and R studio (2012) and the maps were generated using Arc GIS 10.2. The dengue incidence showed a significant positive correlation with rainfall (p

Suggested Citation

  • PDNN Sirisena & Faseeha Noordeen & Harithra Kurukulasuriya & Thanuja ALAR Romesh & LakKumar Fernando, 2017. "Effect of Climatic Factors and Population Density on the Distribution of Dengue in Sri Lanka: A GIS Based Evaluation for Prediction of Outbreaks," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0166806
    DOI: 10.1371/journal.pone.0166806
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    Cited by:

    1. Kang Liu & Meng Zhang & Guikai Xi & Aiping Deng & Tie Song & Qinglan Li & Min Kang & Ling Yin, 2020. "Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(12), pages 1-22, December.
    2. Zheng, Zhoumin & Xu, Nuo & Khan, Mohsin & Pedersen, Michael & Abdalgader, Tarteel & Zhang, Lai, 2024. "Nonlinear impacts of climate change on dengue transmission in mainland China: Underlying mechanisms and future projection," Ecological Modelling, Elsevier, vol. 492(C).
    3. Ming Sun & Xueyu Jiao, 2023. "Quantitative Identification Study of Epidemic Risk in the Spatial Environment of Harbin City," Sustainability, MDPI, vol. 15(9), pages 1-22, May.
    4. 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.
    5. Orratai Nontapet & Jiraporn Jaroenpool & Sarunya Maneerattanasa & Supaporn Thongchan & Chumpron Ponprasert & Patthanasak Khammaneechan & Cua Ngoc Le & Nirachon Chutipattana & Charuai Suwanbamrung, 2022. "Effects of the Developing and Using a Model to Predict Dengue Risk Villages Based on Subdistrict Administrative Organization in Southern Thailand," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    6. Mazni Baharom & Norfazilah Ahmad & Rozita Hod & Fadly Syah Arsad & Fredolin Tangang, 2021. "The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review," IJERPH, MDPI, vol. 18(21), pages 1-22, October.

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