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Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil

  • S. C. BENNEMA (a1), M. B. MOLENTO (a1), R. G. SCHOLTE (a2), O. S. CARVALHO (a2) and I. PRITSCH (a1)...


Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.


Corresponding author

*Corresponding author: Laboratorio de Doencas Parasitarias, Universidade Federal do Parana, R: dos Funcionários, 1540, Curitiba, PR, CEP: 80035-050, Brazil. E-mail:


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Albisua, I., Arbelaitz, O., Gurrutxaga, I., Martín, J. I., Muguerza, J. M., Pérez, J. and Perona, I. (2009). Obtaining optimal class distribution for decision trees: comparative analysis of CTC and C4.5. Actas de la XIII Conferencia de la Asociación Española para la Inteligencia Artificial. Sevilla, Spain. November, 2009.
Aleixo, M., Freitas, D. F., Dutra, L. H., Malone, J., Martins, I. V. F. and Molento, M. B. (2015). Fasciola hepatica: epidemiology, perspectives in the diagnostic and the use of geoprocessing systems for prevalence studies. Semina 36, 14511466.
Alves, D. P., Carneiro, M. B., Martins, I. V. F., Bernardo, C. C., Donatele, D. M., Pereira Júnior, O. S., Almeida, B. R., Avelar, B. R. and Leão, A. G. C. (2011). Distribution and factors associated with Fasciola hepatica infection in cattle in the south of Espírito Santo State, Brazil. Journal of Venomous Animals and Toxins including Tropical Diseases 17, 271276.
Beck, L. R., Lobitz, B. M. and Wood, B. L. (2000). Remote sensing and human health: new sensors and new opportunities. Emerging Infectious Disease 6, 217227.
Bennema, S. C., Ducheyne, E., Vercruysse, J., Claerebout, E., Hendrickx, G. and Charlier, J. (2010). Relative importance of management, meteorological and environmental factors in the spatial distribution of Fasciola hepatica in dairy cattle in a temperate climate zone. International Journal for Parasitology 41, 225233.
Bennema, S. C., Scholte, R. G. C., Molento, M. B., Medeiros, C. and Carvalho, O. S. (2015). Fasciola hepatica in bovines in Brazil: data availability and spatial distribution. Revista do Instituto de Medicina Tropical de Sao Paulo 56, 3541.
Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference : a Practical Information-theoretic Approach. Springer, New York.
Charlier, J., Bennema, S. C., Caron, Y., Counotte, M., Ducheyne, E., Hendrickx, G. and Vercruysse, J. (2011). Towards assessing fine-scale indicators for the spatial transmission risk of Fasciola hepatica in cattle. Geospatial Health 5, 239245.
Drummond, and Holte, (2003). C4.5 class imbalance and cost sensitivity: why under-sampling beats over-sampling. In Workshop on Learning from Imbalanced Datasets II, ICML, Washington, DC.
Durr, P. A., Tait, N. and Lawson, A. B. (2005). Bayesian hierarchical modelling to enhance the epidemiological value of abattoir surveys for bovine fasciolosis. Preventive Veterinary Medicine 71, 157172.
Dutra, L. H., Molento, M. B., Naumann, C. R. C., Biondo, A. W., Fortes, F. S., Savio, D. and Malone, J. B. (2010). Mapping risk of bovine fasciolosis in the south of Brazil using Geographic Information Systems. Veterinary Parasitology 169, 7681.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. (2009). The WEKA Data Mining Software: An Update . SIGKDD Explorations 11, 1018.
Instituto Brasileiro de Geografia e Estatística (IBGE) (2012).
Instituto Brasileiro de Geografia e Estatística (IBGE) (2016). Censo Agropecuario – 2006, pp. 267. Rio de Janeiro, Brazil.
Malone, J. B., Gommes, R., Hansen, J., Yilma, J. M., Slingenberg, J., Snijders, F., Nachtergaele, F. and Ataman, E. (1998). A geographic information system on the potential distribution and abundance of Fasciola hepatica and F. gigantica in east Africa based on Food and Agriculture Organization databases. Veterinary Parasitology 78, 87101.
Martins-Bedê, F. T., Dutra, L. V., Freitas, C. C., Guimarães, R. J. P. S., Amaral, R. S., Drummond, S. C. and Carvalho, O. S. (2010). Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators. Memorias do Instituto Oswaldo Cruz 105, 541548.
McCann, C. M., Baylis, M. and Wiliams, D. J. (2010). Seroprevalence and spatial distribution of Fasciola hepatica-infected dairy herds in England and Wales. Veterinary Record 166, 612617.
Medeiros, C., Scholte, R. C., D’ávila, S., Lima Caldeira, R. and Carvalho, O. S. (2014). Spatial distribution of Lymnaeidae (Mollusca, Basommatophora), intermediate host of Fasciola hepatica Linnaeus, 1758 (Trematoda, Digenea) in Brazil. Revista do Instituto de Medicina Tropical de Sao Paulo 56, 235252.
Oliveira, D. R., Ferreira, D. M., Stival, C. C., Romero, F., Cavagnolli, F., Kloss, A., Araujo, F. B. and Molento, M. B. (2008). Triclabendazole resistance involving Fasciola hepatica in sheep and goats during an outbreak in Almirante Tamandare, Paraná, Brazil. Brazilian Journal of Veterinary Parasitology 17(S1), 149153.
Oliveira, E. L. (2008). Prevalência e fatores associados à distribuição da Fasciola hepatica (Linnaeus, 1758) em bovinos dos municípios de Careaçú e Itajubá, região da bacia do rio Sapucaí, Minas Gerais . dissertation. Universidade Federal de Minas Gerais, Belo Horizonte.
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann. Publishers Inc., San Francisco, CA, USA.
Scholte, R. C., Carvalho, O. S., Malone, J. B., Utzinger, J. and Vounatsou, P. (2012). Spatial distribution of Biomphalaria spp., the intermediate host snails of Schistosoma mansoni, in Brazil. Geospat Health 6, S95S101.
Silva, A. E. P., Freitas, C. C., Dutra, L. V. and Molento, M. B. (2016). Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil. Veterinary Parasitology 217, 713.
Torgerson, P. and Claxton, J. (1999). Epidemiology and control. In Fasciolosis (ed. Dalton, J. P.), pp. 113149. CABI Publishing, Wallingford, USA.
Tum, S., Puotinen, M. L. and Copeman, D. B. (2004). A geographic information systems model for mapping risk of fasciolosis in cattle and buffaloes in Cambodia. Veterinary Parasitology 122, 141149.
Valencia-López, V., Malone, J. B., Gómez Carmona, C. and Velásquez, L. E. (2012). Climate-based risk models for Fasciola hepatica in Colombia. Geospatial Health 6, S75S85.
Waltari, E., Hijmans, R. J., Peterson, A. T., Nyari, A. S., Perkins, S. L. and Guralnick, R. (2007). Locating pleistocene refugia: comparing phylogeographic and ecological niche model predictions. PLoS ONE 2, 563.
Witten, I. H. and Frank, E. (2005) Data Mining: Practical Machine learning Tools and Techniques, 2nd Edn. Morgan Kaufmann Press, San Francisco, USA.


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Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil

  • S. C. BENNEMA (a1), M. B. MOLENTO (a1), R. G. SCHOLTE (a2), O. S. CARVALHO (a2) and I. PRITSCH (a1)...


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