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

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 war...

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Autores principales:	Gayan P. Withanage, Malika Gunawardana, Sameera D. Viswakula, Krishantha Samaraweera, Nilmini S. Gunawardena, Menaka D. Hapugoda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/453c4009d79f460abbff0afb97df7a26
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spelling	oai:doaj.org-article:453c4009d79f460abbff0afb97df7a262021-12-02T14:21:42ZMultivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)10.1038/s41598-021-83204-12045-2322https://doaj.org/article/453c4009d79f460abbff0afb97df7a262021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83204-1https://doaj.org/toc/2045-2322Abstract 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.Gayan P. WithanageMalika GunawardanaSameera D. ViswakulaKrishantha SamaraweeraNilmini S. GunawardenaMenaka D. HapugodaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gayan P. Withanage Malika Gunawardana Sameera D. Viswakula Krishantha Samaraweera Nilmini S. Gunawardena Menaka D. Hapugoda Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)
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.
format	article
author	Gayan P. Withanage Malika Gunawardana Sameera D. Viswakula Krishantha Samaraweera Nilmini S. Gunawardena Menaka D. Hapugoda
author_facet	Gayan P. Withanage Malika Gunawardana Sameera D. Viswakula Krishantha Samaraweera Nilmini S. Gunawardena Menaka D. Hapugoda
author_sort	Gayan P. Withanage
title	Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)
title_short	Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)
title_full	Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)
title_fullStr	Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)
title_full_unstemmed	Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)
title_sort	multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using geographic information system (gis)
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/453c4009d79f460abbff0afb97df7a26
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Multivariate spatio-temporal approach to identify vulnerable localities in dengue risk areas using Geographic Information System (GIS)

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