A global network for the control of snail-borne disease using satellite surveillance and geographic information systems

Malone, John B.; Bergquist, N.R.; Huh, Oscar K.; Bavia, Maria Emília; Bernardi, Michele; Bahy, Mohamed M. El; Fuentes, Màrius V.; Kristensen, Troels; McCarroll, Jennifer C; Yilma, J.M.; Zhou, X.N.

doi:10.1016/s0001-706x(01)00098-5

Cited by 39 publications

(23 citation statements)

References 4 publications

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“…Based on the recommendations of Malone et al (2001) and following the proposal of Fuentes (2004), Fuentes et al (2004Fuentes et al ( , 2005) developed a GIS model for zoonotic fasciolosis risk assessment in the South American Andean region which allows the identification of areas requiring the implementation of control activities through the classification of the degree of transmission risk for this hepatic disease. This model, based on the results obtained by Fuentes et al ( , 2001 in the Northern Bolivian Altiplano, also includes the calculation of the Wb-bs index accumulated values (Fig.…”

Section: The Andean Fasciolosis Gis Modelmentioning

confidence: 99%

Remote sensing and climate data as a key for understanding fasciolosis transmission in the Andes: review and update of an ongoing interdisciplinary project

Fuentes

2006

Geospat Health

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Abstract. Fasciolosis caused by Fasciola hepatica in various SouthAmerican countries located on the slopes of the Andes has been recognized as an important public health problem. However, the importance of this zoonotic hepatic parasite was neglected until the last decade. Countries such as Peru and Bolivia are considered to be hyperendemic areas for human and animal fasciolosis, and other countries such as Chile, Ecuador, Colombia and Venezuela are also affected. At the beginning of the 1990s a multidisciplinary project was launched with the aim to shed light on the problems related to this parasitic disease in the Northern Bolivian Altiplano. A few years later, a geographic information system (GIS) was incorporated into this multidisciplinary project analysing the epidemiology of human and animal fasciolosis in this South American Andean region. Various GIS projects were developed in some Andean regions using climatic data, climatic forecast indices and remote sensing data.Step by step, all these GIS projects concerning the forecast of the fasciolosis transmission risk in the Andean mountain range were revised and in some cases updated taking into account new data. The first of these projects was developed on a regional scale for the central Chilean regions and the proposed model was validated on a local scale in the Northern Bolivian Altiplano. This validated mixed model, based on both fasciolosis climatic forecast indices and normalized difference vegetation index values from Advanced Very High Resolution Radiometer satellite sensor, was extrapolated to other human and/or animal endemic areas of Peru and Ecuador. The resulting fasciolosis risk maps make it possible to show the known human endemic areas of, mainly, the Peruvian Altiplano, Cajamarca and Mantaro Peruvian valleys, and some valleys of the Ecuadorian Cotopaxi province. Nevertheless, more climate and remote sensing data, as well as more accurate epidemiological reports, have to be incorporated into these GIS projects, which should be considered the key in understanding fasciolosis transmission in the Andes.

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Section: The Andean Fasciolosis Gis Modelmentioning

confidence: 99%

Remote sensing and climate data as a key for understanding fasciolosis transmission in the Andes: review and update of an ongoing interdisciplinary project

Fuentes

2006

Geospat Health

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“…Following the recommendations of the GNOSIS (GIS Network On Snail-borne Infections with special reference to Schistosomiasis) research group 30 , all GIS are based on the use of a database (epidemiological, environmental, remote sensing type, etc) which, together with the application of specialized software, is reflected in cartographic form with subsequent analysis. This approach allows the development of an epidemiological model of the disease in the area under study.…”

Section: Presentation Of the Proposalmentioning

confidence: 99%

Proposal of a Geographic Information System for modeling zoonotic fasciolosis transmission in the Andes

Fuentes¹

2004

Parasitol. latinoam.

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“…Bavia et al (2001) and in Ethiopia by Malone et al (2001) showed that the temperature was a good predictor of the disease.…”

Section: Resultsmentioning

confidence: 98%

Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

Martins-Bedé

Dutra

Freitas

et al. 2010

Mem. Inst. Oswaldo Cruz

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Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MGThe disease is related to social and behavioural factors, particularly inadequate public and environmental sanitation and a low level of education about health in the populations involved (Doumenge et al. 1987).Once schistosomiasis risk is identified by both environmental and social factors, new computerised analytical tools, known as Geographic Information Systems and Remote Sensing Data Analysis, have been used to map epidemiological data or analyse satellite images .In Brazil, geo-processing tools have been used in the study of schistosomiasis in the states of Bahia (BA) and Minas Gerais (MG). These studies have provided risk maps for schistosomiasis infection on a municipality basis by using multiple regression analyses that include environmental features, a priori disease prevalence data and other spatial data (Bavia et al. 1999, 2008, Martins-Bedê et al. 2009).In the present paper, a standard data-mining technique, the decision tree, was used to identify the severity of disease prevalence. This technique is based on a recursive Predictive models are used to classify different samples whose values or labels are not known. In this paper, a decision tree model is used to classify the schistosomiasis prevalence risk for the whole state. Remote sensing data and spatial sociological indicators are used to map potential risk areas which are not covered by the schistosomiasis control program. This map of potential risk areas can be used by a decision maker to evaluate municipalities outside of the program that have similar environmental and social conditions to the municipalities covered by the program. Moreover, the map can serve as a guide for future disease-control efforts in the state. MATERIALS AND METHODSStudy area -The study area was MG, Brazil. MG is 590,000 square kilometres in size and is politically divided into 853 cities; the area has a tropical climate and includes approximately 18 million inhabitants (IBGE 2008).

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A global network for the control of snail-borne disease using satellite surveillance and geographic information systems

Cited by 39 publications

References 4 publications

Remote sensing and climate data as a key for understanding fasciolosis transmission in the Andes: review and update of an ongoing interdisciplinary project

Remote sensing and climate data as a key for understanding fasciolosis transmission in the Andes: review and update of an ongoing interdisciplinary project

Proposal of a Geographic Information System for modeling zoonotic fasciolosis transmission in the Andes

Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

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