Molecular Medicine Unit, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka.
Postgraduate Institute of Science, University of Peradeniya, Peradeniya, Sri Lanka.
Sci Rep. 2021 Feb 18;11(1):4080. doi: 10.1038/s41598-021-83204-1.
Dengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.
登革热是目前斯里兰卡最重要的虫媒传染病之一，给该国带来了巨大的经济和社会负担。目前既没有疫苗，也没有药物，因此控制病媒是该国控制疾病传播的最佳方法。因此，迫切需要建立早期预警系统。本研究旨在开发基于地理信息系统（GIS）的多元分析模型，以检测斯里兰卡甘帕哈区登革热的风险热点，从而控制疾病传播。在该地区的四个高风险区域中，使用单独的层为患者发病地点、阳性滋生容器、道路、总建筑物、公共场所、土地利用图和海拔，分别开发了风险模型和空间泊松点过程模型。使用核密度和欧几里得距离函数以及最小允许距离参数识别每个研究层与患者发病的空间相关性。风险模型的输出文件表明，高风险地区靠近道路且与植被覆盖度密切相关，而泊松模型则突出了高强度地区与公共场所的接近程度以及登革热人工蓄水池的可能性。后者的模型进一步表明，在高风险地区，登革热病例在半径约 150 m 的范围内呈聚集性，这表明这些区域在未来的病媒监测中需要高度关注。
