Literature DB >> 18692017

Schistosomiasis risk estimation in Minas Gerais State, Brazil, using environmental data and GIS techniques.

Ricardo J P S Guimarães1, Corina C Freitas, Luciano V Dutra, Ana C M Moura, Ronaldo S Amaral, Sandra C Drummond, Ronaldo G C Scholte, Omar S Carvalho.   

Abstract

The influence of climate and environmental variables to the distribution of schistosomiasis has been assessed in several previous studies. Also Geographical Information System (GIS), is a tool that has been recently tested for better understanding the spatial disease distribution. The objective of this paper is to further develop the GIS technology for modeling and control of schistosomiasis using meteorological and social variables and introducing new potential environmental-related variables, particularly those produced by recently launched orbital sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). Three different scenarios have been analyzed, and despite of not quite large determination factor, the standard deviation of risk estimates was considered adequate for public health needs. The main variables selected as important for modeling purposes was topographic elevation, summer minimum temperature, the NDVI vegetation index, and the social index HDI91.

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Year:  2008        PMID: 18692017     DOI: 10.1016/j.actatropica.2008.07.001

Source DB:  PubMed          Journal:  Acta Trop        ISSN: 0001-706X            Impact factor:   3.112


  6 in total

1.  Use of indicator kriging to investigate schistosomiasis in minas gerais state, Brazil.

Authors:  Ricardo J P S Guimarães; Corina C Freitas; Luciano V Dutra; Carlos A Felgueiras; Sandra C Drummond; Sandra H C Tibiriçá; Guilherme Oliveira; Omar S Carvalho
Journal:  J Trop Med       Date:  2012-01-12

2.  Spatial analysis of Schistosomiasis in Hubei Province, China: a GIS-based analysis of Schistosomiasis from 2009 to 2013.

Authors:  Yan-Yan Chen; Xi-Bao Huang; Ying Xiao; Yong Jiang; Xiao-Wei Shan; Juan Zhang; Shun-Xiang Cai; Jian-Bing Liu
Journal:  PLoS One       Date:  2015-04-07       Impact factor: 3.240

3.  Comprehensive Risk Assessment of Schistosomiasis Epidemic Based on Precise Identification of Oncomelania hupensis Breeding Grounds-A Case Study of Dongting Lake Area.

Authors:  Jun Xu; Xiao Ouyang; Qingyun He; Guoen Wei
Journal:  Int J Environ Res Public Health       Date:  2021-02-17       Impact factor: 3.390

4.  Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping.

Authors:  Mark A Deka
Journal:  Trop Med Infect Dis       Date:  2022-01-19

5.  Schistosomiasis mansoni incidence data in Rwanda can improve prevalence assessments, by providing high-resolution hotspot and risk factors identification.

Authors:  E Nyandwi; A Veldkamp; S Amer; C Karema; I Umulisa
Journal:  BMC Public Health       Date:  2017-10-25       Impact factor: 3.295

6.  Evaluation of diagnostic methods for the detection of intestinal schistosomiasis in endemic areas with low parasite loads: Saline gradient, Helmintex, Kato-Katz and rapid urine test.

Authors:  Warllem Junio Oliveira; Fernanda do Carmo Magalhães; Andressa Mariana Saldanha Elias; Vanessa Normandio de Castro; Vivian Favero; Catieli Gobetti Lindholz; Áureo Almeida Oliveira; Fernando Sergio Barbosa; Frederico Gil; Maria Aparecida Gomes; Carlos Graeff-Teixeira; Martin Johannes Enk; Paulo Marcos Zech Coelho; Mariângela Carneiro; Deborah Aparecida Negrão-Corrêa; Stefan Michael Geiger
Journal:  PLoS Negl Trop Dis       Date:  2018-02-22
  6 in total

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