Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study

Khormi, Hassan M.; Kumar, Lalit

doi:10.1016/j.scitotenv.2011.08.028

Cited by 104 publications

(79 citation statements)

References 12 publications

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“…As official Census units of the Auckland region, it was more practical to use polygons as the basis of the spatial analysis conducted. Area based spatial analysis was employed in a variety of studies including historical analysis (Zhang et al, 2011), dengue fever risk (Khormi & Kumar, 2011) and landcover relationships with ecosystems (Shaker, Craciun, & Gradinaru, 2010). Road information on the other hand, presents another reference to confirm stray cat pickup locations at respective areas.…”

Section: Data Acquisition From Ordinary Sourcesmentioning

confidence: 99%

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Aguilar

Farnworth

2012

Applied Geography

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Stray cats are a common feature of urban landscapes and are associated with issues of animal welfare and negative environmental impacts. Management, planning and decision-making require readily accessible information on stray cats. However, much of the existing data is not immediately useful for a geographic information system (GIS) in terms of format, content and explicit location information. Spreadsheets we obtained from a single large shelter in the Auckland region. They contained records of stray cat pickups and admissions for an entire year (n = 8573) of which 56.4% (n = 4834) contained data that could be processed to derive relevant spatial information. The resulting data consisted of identified roads and areas of Auckland where the stray cats were found. Published census databases and shapefiles were matched with the data to build a GIS of stray cats. Global and local regression analysis was employed to discover spatial distribution characteristics including the identification of areas with relatively high and low concentrations of stray cats and to explore relationships between socioeconomic condition and stray cat density. Significant clustering is more evident in South Auckland than elsewhere in the region. Specific geographical information is valuable, not only for understanding population dynamics of stray cats, but also to allow spatial and temporal targeting of resources to minimise their impact and promote responsible ownership.

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Section: Data Acquisition From Ordinary Sourcesmentioning

confidence: 99%

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Aguilar

Farnworth

2012

Applied Geography

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“…Khormi and Kumar (2011) previously found that areas in Jeddah with a low risk for DF also have a low mean population density (2,107 per km²), whereas areas of medium risk have a medium population density (12,880 per km²) and areas that carry the highest risk have a very high population density (19,728 per km²). This is borne out in this study as Fig.…”

Section: Discussionmentioning

confidence: 98%

Modeling spatio-temporal risk changes in the incidence of dengue fever in Saudi Arabia: a geographical information system case study

2011

Self Cite

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Abstract. The aim of this study was to use geographical information systems to demonstrate the Dengue fever (DF) risk on a monthly basis in Jeddah, Saudi Arabia with the purpose to provide documentation serving to improve surveillance and monitor the Aedes aegypti mosquito vector. Getis-Ord Gi* statistics and a frequency index covering a five-year period (2006)(2007)(2008)(2009)(2010) were used to map DF and model the risk spatio-temporally. The results show that monthly hotspots were mainly concentrated in central Jeddah districts and that the pattern changes considerably with time. For example, on a yearly basis, for the month of January, the Burman district was identified as a low risk area in 2006, a high-risk area in 2007, medium risk in 2008, very low risk in 2009 and low risk in 2010. The results demonstrate that it would be useful to follow the monthly DF pattern, based on the average weekly frequency, as this can facilitate the allocation of resources for the treatment of the disease, preventing its prevalence and monitoring its vector.

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“…The grid size was approached based on the approximate size of one rukun tetangga (RT) or block in Bandung city that consists of about 30-75 houses (each house estimated as about 72 m 2 ) according to the city regulations. This method was a modification from that used by Kumar et al, who applied a 100 m × 100 m grid for built-up land density [34,40]. The incidence of the disease was then obtained and used for analyzing DDP using Hotspot analysis or Getis-Ord Gi (GiZ) in ArcGIS 10.1 toolbox that can output numeric continuous data and which can identify statistically significant hotspots, random and also dispersed patterns [1,41].…”

Section: Discussionmentioning

confidence: 99%

“…Dengue case data from 1 January-31 December 2012 were obtained from Bandung city health service. It has been a common problem that dengue surveillance data are reported in areal units [34]. However, we used dengue disease patient data in point units, which comprised addresses of patients, diagnoses, and dates of symptoms before hospital admission.…”

Section: Study Area and Datamentioning

confidence: 99%

Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

Rinawan

Tateishi

Raksanagara

et al. 2015

IJGI

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Dengue disease incidence is related with the construction of a house roof, which is an Aedes mosquito habitat. This study was conducted to classify pitch roof (PR) and flat roof (FR) surfaces using pan-sharpened Worldview 2 to identify dengue disease patterns (DDPs) and their association with DDP. A Supervised Minimum Distance classifier was applied to 653 training data from image object segmentations: PR (81 polygons), FR (50), and non-roof (NR) class (522). Ground validation of 272 pixels (52 for PR, 51 for FR, and 169 for NR) was done using a global positioning system (GPS) tool. Getis-Ord score pattern analysis was applied to 1154 dengue disease incidence with address-approach-based data with weighted temporal value of 28 days within a 1194 m spatial radius. We used ordinary least squares (OLS) and geographically weighted regression (GWR) to assess

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Modeling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study

Cited by 104 publications

References 12 publications

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

Modeling spatio-temporal risk changes in the incidence of dengue fever in Saudi Arabia: a geographical information system case study

Pitch and Flat Roof Factors’ Association with Spatiotemporal Patterns of Dengue Disease Analysed Using Pan-Sharpened Worldview 2 Imagery

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