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

Published online by Cambridge University Press:  14 August 2017

S. C. BENNEMA ,
M. B. MOLENTO Open the ORCID record for M. B. MOLENTO [Opens in a new window] ,
R. G. SCHOLTE ,
O. S. CARVALHO  and
I. PRITSCH
S. C. BENNEMA
Affiliation:
Laboratorio de Doencas Parasitarias, Universidade Federal do Parana, R: dos Funcionários, 1540, Curitiba, PR, CEP: 80035-050, Brazil
M. B. MOLENTO*
Affiliation:
Laboratorio de Doencas Parasitarias, Universidade Federal do Parana, R: dos Funcionários, 1540, Curitiba, PR, CEP: 80035-050, Brazil
R. G. SCHOLTE
Affiliation:
Laboratorio de Helmintologia e Malacologia, Fundação Oswaldo Cruz, Av: Augusto Lima, 1715. Belo Horizonte, MG, CEP: 21040-900, Brazil
O. S. CARVALHO
Affiliation:
Laboratorio de Helmintologia e Malacologia, Fundação Oswaldo Cruz, Av: Augusto Lima, 1715. Belo Horizonte, MG, CEP: 21040-900, Brazil
I. PRITSCH
Affiliation:
Laboratorio de Doencas Parasitarias, Universidade Federal do Parana, R: dos Funcionários, 1540, Curitiba, PR, CEP: 80035-050, Brazil
*
*Corresponding author: Laboratorio de Doencas Parasitarias, Universidade Federal do Parana, R: dos Funcionários, 1540, Curitiba, PR, CEP: 80035-050, Brazil. E-mail: molento@ufpr.br
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Summary

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.

Keywords

Liver flukeruminantsprediction mapgeospatial diagnostic
Type
Research Article
Information
Parasitology , Volume 144 , Issue 13 , November 2017 , pp. 1677 - 1685
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Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

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.Google Scholar
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, 1451–1466.Google Scholar
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, 271–276.Google Scholar
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, 217–227.CrossRefGoogle ScholarPubMed
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, 225–233.CrossRefGoogle Scholar
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, 35–41.CrossRefGoogle Scholar
Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference : a Practical Information-theoretic Approach. Springer, New York.Google Scholar
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, 239–245.Google Scholar
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.Google Scholar
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, 157–172.Google Scholar
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, 76–81.Google Scholar
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. (2009). The WEKA Data Mining Software: An Update . SIGKDD Explorations 11, 10–18.Google Scholar
Instituto Brasileiro de Geografia e Estatística (IBGE) (2012). http://www.ibge.gov.br.Google Scholar
Instituto Brasileiro de Geografia e Estatística (IBGE) (2016). Censo Agropecuario – 2006, pp. 267. Rio de Janeiro, Brazil.Google Scholar
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, 87–101.Google Scholar
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, 541–548.Google Scholar
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, 612–617.CrossRefGoogle ScholarPubMed
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, 235–252.CrossRefGoogle ScholarPubMed
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), 149–153.Google ScholarPubMed
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.Google Scholar
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann. Publishers Inc., San Francisco, CA, USA.Google Scholar
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, S95–S101.Google Scholar
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, 7–13.CrossRefGoogle Scholar
Torgerson, P. and Claxton, J. (1999). Epidemiology and control. In Fasciolosis (ed. Dalton, J. P.), pp. 113–149. CABI Publishing, Wallingford, USA.Google Scholar
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, 141–149.Google Scholar
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, S75–S85.Google Scholar
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.CrossRefGoogle ScholarPubMed
Witten, I. H. and Frank, E. (2005) Data Mining: Practical Machine learning Tools and Techniques, 2nd Edn. Morgan Kaufmann Press, San Francisco, USA.Google Scholar