Multivariate spatio‑temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)

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dc.contributor.author Withanage, Gayan P.
dc.contributor.author Gunawardana, Malika
dc.contributor.author Viswakula, S.D.
dc.contributor.author Samaraweera, Krishantha
dc.contributor.author Gunawardena, Nilmini S.
dc.contributor.author Hapugoda, Menaka
dc.date.accessioned 2021-10-01T10:28:23Z
dc.date.available 2021-10-01T10:28:23Z
dc.date.issued 2021
dc.identifier.citation Withanage, G.P., Gunawardana, M., Viswakula, S.D. et al. Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS). Sci Rep 11, 4080 (2021). https://doi.org/10.1038/s41598-021-83204-1 en_US
dc.identifier.uri https://doi.org/10.1038/s41598-021-83204-1
dc.identifier.uri http://archive.cmb.ac.lk:8080/xmlui/handle/70130/6170
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.title Multivariate spatio‑temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS) en_US
dc.type Article en_US


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