Literature DB >> 15275115

Geographic information systems and the distribution of Schistosoma mansoni in the Nile delta.

J B Malone1, M S Abdel-Rahman, M M El Bahy, O K Huh, M Shafik, M Bavia.   

Abstract

New computer-based sensor technology and geographic methods have led to emerging interest in use of satellite environmental assessment tools for design of disease control programs, especially for those that are vector borne. The long-range goal of work reported here by John Malone and colleagues on behalf of this Egyptian Ministry of Health-USAID Schistosomiasis Research Project team (Box 1) is to utilize data from sensor systems on board earth-observing satellites to develop more-sensitive disease-prediction and -control models. If successful, methods developed may provide a potentially vital capability for use by disease control program managers, particularly in less-developed countries, where mapping resources are not well advanced. Longer term, broader basic questions on the interaction of environment and disease in anticipation of predicted global climate change may be addressed. These studies focused on the lower Nile river basin of Egypt. The specific objective was to link data on environmental requirements for propagation and transmission of schistosomiasis with parameters measurable from space.

Entities:  

Year:  1997        PMID: 15275115     DOI: 10.1016/s0169-4758(97)01009-0

Source DB:  PubMed          Journal:  Parasitol Today        ISSN: 0169-4758


  11 in total

Review 1.  Climate change and health research in the Eastern Mediterranean Region.

Authors:  Rima R Habib; Kareem El Zein; Joly Ghanawi
Journal:  Ecohealth       Date:  2010-07-24       Impact factor: 3.184

2.  Meta-analysis of the diagnostic efficiency of the questionnaires screening for schistosomiasis.

Authors:  Fen Yang; Xiao-Dong Tan; Bei Liu; Chongming Yang; Zi-Ling Ni; Xu-Dong Gao; Ying Wang
Journal:  Parasitol Res       Date:  2015-06-28       Impact factor: 2.289

3.  Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data.

Authors:  S Brooker; S I Hay; W Issae; A Hall; C M Kihamia; N J Lwambo; W Wint; D J Rogers; D A Bundy
Journal:  Trop Med Int Health       Date:  2001-12       Impact factor: 2.622

4.  Tools from ecology: useful for evaluating infection risk models?

Authors:  Simon Brooker; Simon I Hay; Don A P Bundy
Journal:  Trends Parasitol       Date:  2002-02

5.  Pathogenic landscapes: interactions between land, people, disease vectors, and their animal hosts.

Authors:  Eric F Lambin; Annelise Tran; Sophie O Vanwambeke; Catherine Linard; Valérie Soti
Journal:  Int J Health Geogr       Date:  2010-10-27       Impact factor: 3.918

6.  Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome.

Authors:  G E Glass; J E Cheek; J A Patz; T M Shields; T J Doyle; D A Thoroughman; D K Hunt; R E Enscore; K L Gage; C Irland; C J Peters; R Bryan
Journal:  Emerg Infect Dis       Date:  2000 May-Jun       Impact factor: 6.883

7.  Modeling the risk of transmission of schistosomiasis in Akure North Local Government Area of Ondo State, Nigeria using satellite derived environmental data.

Authors:  Oluwaremilekun G Ajakaye; Oluwatola I Adedeji; Paul O Ajayi
Journal:  PLoS Negl Trop Dis       Date:  2017-07-12

8.  Environmental factors and the risk of urinary schistosomiasis in Ile Oluji/Oke Igbo local government area of Ondo State.

Authors:  Oluwaremilekun G Ajakaye; Titus Adeniyi Olusi; M O Oniya
Journal:  Parasite Epidemiol Control       Date:  2016-03-31

9.  Predicting the impact of long-term temperature changes on the epidemiology and control of schistosomiasis: a mechanistic model.

Authors:  Tara D Mangal; Steve Paterson; Andrew Fenton
Journal:  PLoS One       Date:  2008-01-16       Impact factor: 3.240

10.  Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria.

Authors:  Uwem F Ekpo; Chiedu F Mafiana; Clement O Adeofun; Adewale Rt Solarin; Adewumi B Idowu
Journal:  BMC Infect Dis       Date:  2008-05-31       Impact factor: 3.090

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