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

Abstract

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.

Fascioliasis in buffaloes: A 5-year forecast analysis of the disease based on a 15-year survey in Brazil

“…The International Panel on Climate Change (CLINE, 2007) reported that countries in Latin America may observe an increase in disease incidence, due to the increase of inundation and specific vectors. Therefore, the increase in temperature and precipitation will change the prevalence of fascioliasis, as was also determined in Brazil by Bennema et al (2017) and Dutra et al (2010). Silva et al (2016) noted that La Nina years affected bovine fascioliasis (temperature and rainfall).…”

“…In cattle, previous studies have also shown that the prevalence of fascioliasis was mainly in the southern states (ALEIXO et al, 2015;DUTRA et al, 2010;SILVA et al, 2016), with an economic loss of approximately US$ 210 million/year (MOLENTO et al, 2018). Bennema et al (2017) have also determined that the best suitability area for F. hepatica development, based on a range of isothermality data was the south region of the country. Other studies have also investigated the climate effects and surveillance data for fascioliasis in Europe (CAMINADE et al, 2015;NOVOBILSKÝ et al, 2015).…”

“…Considering the several characteristics of fascioliasis lifecycle, it can be classified as a disease that is susceptible to the impacts of climatic variations. Studies have shown the influence of the weather on fascioliasis infection in cattle (BENNEMA et al, 2014(BENNEMA et al, , 2017DUTRA et al, 2010). An increase in rainfall was strongly correlated with a higher occurrence of the disease in southern Brazil (DUTRA et al, 2010).…”

“…The correlation between a disease and its geographic and climatic location is possible with the use of geographic information systems, creating epidemiological maps for viewing and evidencing risk factors. Based on these tools, Bennema et al (2017), using a range of isothermality, showed that a large part of Brazil is suitable for F. hepatica. Aleixo et al (2015), using a kernel analysis, determined the density of livers infected with F. hepatica in Brazil from 2003 to 2008, identifying the localities with higher prevalence of fascioliasis in cattle.…”

Fasciola hepaticainfection in cattle and the use of simulation models for endemic areas

“…The disease may cause production losses of US$90-210 million annually (Rahmeto et al, 2010;Molento et al, 2018), and Dutra et al (2010) reported that F. hepatica is endemic in Rio Grande do Sul (RS), a state located in the south of Brazil, with an average prevalence of 14.3%. The distribution of F. hepatica in Brazil is extensive in the southern region and its presence reaches up to the south border of the Amazon region (Bennema et al, 2014(Bennema et al, , 2017.…”