Climate change on the forecasted risk of bovine fasciolosis in Espírito Santo state, Brazil

Abstract

The climate change expected for the coming years can cause large economic losses and a strong impact on intestinal parasites of ruminants throughout the world. In this sense, organisms belonging to the class trematoda seem to be highly sensitive to any changes in the patterns of temperature and rainfall caused by possible climate change. So, maps were elaborated forecasting current and future risk to Fasciola hepatica in the state of Espírito Santo, Southeast of Brazil, using as a base increases in the temperature ranging from 1°C to 5°C. Environmental and climatic factors like temperature, rainfall, altitude and declivity were used for generation of maps bioclimatic risk of fasciolosis through of the ArcGIS/ArcInfo 10.1 Software. High-risk areas resulted in a total of 35.42% for the current risk and tended to decrease with increases in temperature over the next 100 years, which favored a reduction of 35.42% to 33.84% in these regions. The places included in the of low risk areas showed significant increases in their areas for temperatures up to 5°C with values ranging from 24.65% to 28.26% of their areas. These forecasting models using increases in temperatures in the generation of risk maps to F. hepatica were first made in Brazil and like the others, represented a tendency to aid in policy making animal and human health oriented regions shown to be potentially suitable for the risk of bovine fasciolosis. Key words: Geographic Information System (GIS), climate change, Fasciola hepatica, cattle ResumoAs mudanças climáticas esperadas para os próximos anos podem ocasionar grande impacto sobre as parasitoses intestinais de ruminantes em todo o mundo. Neste sentido, organismos pertencentes à classe Trematoda parecem ser altamente sensíveis a alterações nos padrões de temperatura e precipitação causados por eventuais alterações climáticas. Assim, foram elaborados mapas de previsão de risco atual e futuro para Fasciola hepatica no estado do Espírito Santo, Sudeste do Brasil, utilizando como base o aumentos na temperatura que variaram de 1°C a 5°C. Fatores ambientais e climáticos, como a temperatura, precipitação, a altitude e a declividade foram utilizados para a geração de mapas de risco para fasciolose por meio do programa ArcGIS / ArcInfo 10.1. As áreas de alto risco resultaram em um total de 35,42% para o risco atual e tenderam a diminuir com o aumento da temperatura para os próximos 100 anos, o que favoreceu uma redução de 35,42% para 33,84% nestas regiões. As localidades

Spatial analysis and risk mapping of Fasciola hepatica infection in dairy cattle at the Peruvian central highlands

“…We have developed a model for fasciolosis risk based on an Artificial Neural Networks model (ANN) to assess the environmental factors that could best explain the presence of fasciolosis during the dry and rainy seasons. This methodology differs from other similar works, where other techniques, including statistical procedures, such as the Analytic Hierarchy Process ( Freitas et al, 2014 ) or variants of machine learning, such as Random Forest or Boosted Regression Trees ( Ducheyne et al, 2015 ). Although not commonly used for epidemiological studies on fasciolosis, ANN is also based on machine learning.…”

“…Regarding reports from South America, in the regions of Espirito Santo in Brazil, a theoretical algorithm, based on data previously published, was used to perform risk analysis for fasciolosis ( Vilhena Freire Martins et al, 2012 ). Freitas et al (2014) developed a forecasting risk model for F. hepatica infection in the same Brazilian region using linear regressions and ranked different variables according to a decision matrix created by an Analytical Hierarchy Process (AHP). A much more elaborated approach was performed in Colombia, where a climate-based risk model for bovine fasciolosis at a country level was constructed using prevalence data and a mathematical model of maximum entropy (MaxEnt), originally developed for modeling of ecological niches ( Valencia-López et al, 2012 ).…”