Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016
Abstract Dengue is a vector-borne disease causing high morbidity and mortality in tropical and subtropical countries. Urbanization, globalization, and lack of effective mosquito control have lead to dramatically increased frequency and magnitude of dengue epidemic in the past 40 years. The virus and...
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doaj-94c7dede302a48f89bda3f65d5f0e08f2020-12-08T03:17:51ZengNature Publishing GroupScientific Reports2045-23222017-10-017111210.1038/s41598-017-13163-zSpatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016Wei Sun0Ling Xue1Xiaoxue Xie2Harbin Engineering University, Department of MathematicsHarbin Engineering University, Department of MathematicsHarbin Engineering University, Department of MathematicsAbstract Dengue is a vector-borne disease causing high morbidity and mortality in tropical and subtropical countries. Urbanization, globalization, and lack of effective mosquito control have lead to dramatically increased frequency and magnitude of dengue epidemic in the past 40 years. The virus and the mosquito vectors keep expanding geographically in the tropical regions of the world. Using the hot spot analysis and the spatial-temporal clustering method, we investigated the spatial-temporal distribution of dengue in Sri Lanka from 2012 to 2016 to identify spatial-temporal clusters and elucidate the association of climatic factors with dengue incidence. We detected two important spatial-temporal clusters in Sri Lanka. Dengue incidences were predicted by combining historical dengue incidence data with climate data, and hot and cold spots were forecasted using the predicted dengue incidences to identify areas at high risks. Targeting the hot spots during outbreaks instead of all the regions can save resources and time for public health authorities. Our study helps better understand how climatic factors impact spatial and temporal spread of dengue virus. Hot spot prediction helps public health authorities forecast future high risk areas and direct control measures to minimize cost on health, time, and economy.https://doi.org/10.1038/s41598-017-13163-z |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Sun Ling Xue Xiaoxue Xie |
spellingShingle |
Wei Sun Ling Xue Xiaoxue Xie Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016 Scientific Reports |
author_facet |
Wei Sun Ling Xue Xiaoxue Xie |
author_sort |
Wei Sun |
title |
Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016 |
title_short |
Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016 |
title_full |
Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016 |
title_fullStr |
Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016 |
title_full_unstemmed |
Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016 |
title_sort |
spatial-temporal distribution of dengue and climate characteristics for two clusters in sri lanka from 2012 to 2016 |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2017-10-01 |
description |
Abstract Dengue is a vector-borne disease causing high morbidity and mortality in tropical and subtropical countries. Urbanization, globalization, and lack of effective mosquito control have lead to dramatically increased frequency and magnitude of dengue epidemic in the past 40 years. The virus and the mosquito vectors keep expanding geographically in the tropical regions of the world. Using the hot spot analysis and the spatial-temporal clustering method, we investigated the spatial-temporal distribution of dengue in Sri Lanka from 2012 to 2016 to identify spatial-temporal clusters and elucidate the association of climatic factors with dengue incidence. We detected two important spatial-temporal clusters in Sri Lanka. Dengue incidences were predicted by combining historical dengue incidence data with climate data, and hot and cold spots were forecasted using the predicted dengue incidences to identify areas at high risks. Targeting the hot spots during outbreaks instead of all the regions can save resources and time for public health authorities. Our study helps better understand how climatic factors impact spatial and temporal spread of dengue virus. Hot spot prediction helps public health authorities forecast future high risk areas and direct control measures to minimize cost on health, time, and economy. |
url |
https://doi.org/10.1038/s41598-017-13163-z |
work_keys_str_mv |
AT weisun spatialtemporaldistributionofdengueandclimatecharacteristicsfortwoclustersinsrilankafrom2012to2016 AT lingxue spatialtemporaldistributionofdengueandclimatecharacteristicsfortwoclustersinsrilankafrom2012to2016 AT xiaoxuexie spatialtemporaldistributionofdengueandclimatecharacteristicsfortwoclustersinsrilankafrom2012to2016 |
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