2002
DOI: 10.4269/ajtmh.2002.67.480
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A geostatistical analysis of the geographic distribution of lymphatic filariasis prevalence in southern India.

Abstract: Abstract. Gaining a better understanding of the spatial population structure of infectious agents is increasingly recognized as being key to their more effective mapping and to improving knowledge of their overall population dynamics and control. Here, we investigate the spatial structure of bancroftian filariasis distribution using geostatistical methods in an endemic region in Southern India. Analysis of a parasite antigenemia prevalence dataset assembled by sampling 79 villages selected using a World Health… Show more

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Cited by 59 publications
(48 citation statements)
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“…The GWR tool gave separate regression coefficients for each of the sub-district for public sector and private sector in the study area. These coefficients were mapped as raster surfaces, and the population density according to spatially varying regression coefficients was generated using the GWR tool in ArcGis [7]. All the analysis was done using ArcGIS 10.0.…”
Section: Discussionmentioning
confidence: 99%
“…The GWR tool gave separate regression coefficients for each of the sub-district for public sector and private sector in the study area. These coefficients were mapped as raster surfaces, and the population density according to spatially varying regression coefficients was generated using the GWR tool in ArcGis [7]. All the analysis was done using ArcGIS 10.0.…”
Section: Discussionmentioning
confidence: 99%
“…Spatial analysis, the linking of diseases to geographic areas, is a fundamental epidemiologic tool dating back to the earliest days of epidemiology when John Snow linked a London cholera epidemic to a contaminated well. [21][22][23] It has continued to be used as an essential tool in defining the epidemiology of a wide range of infectious diseases [24][25][26][27][28] and is evolving as an important approach to environmental and other areas of epidemiology. [29][30][31][32][33][34][35][36][37] Its relevance to a broad range of health issues such as diabetes, 38 childhood lead poisoning, 39 pediatric burn injuries, 40 fertility, 41 cancer screening, 42 general chronic disease prevention, 43 and health services research 44 is also unfolding.…”
Section: Spatial Analysismentioning
confidence: 99%
“…In recent years, geo-statistics, linear models, neural networks, regression trees, fuzzy systems and other analytical procedures have been used to analyze soil nutrient distributions and are considered good tools for use in understanding nutrient dynamics in the field (Zhang et al 2007;Srividya et al 2002;Liu et al 2006;DeBusk et al 1994;Park and Vlek 2002). Despite the sophisticated analytical techniques available and the recognition of the importance of understanding nutrient variability, however, the nature and degree of soil nutrient variability with respect to landscape position is still poorly understood (Silveira et al 2009;Gallardo 2003).…”
Section: Introductionmentioning
confidence: 99%