Correlation between climate data and land altitude for Fasciola hepatica infection in cattle in Santa Catarina, Brazil.

Fascioliasis is a food-borne parasitic disease that affects a range of animals, including humans caused by Fasciola hepatica. The present study aimed to determine the spatial distribution of bovine fasciolosis and to assess the correlation between the high Positivity Index (PI) and climate data and land altitude, from 2004 to 2008 and 2010 in Santa Catarina (SC), Brazil. Condemned livers of slaughtered animals were obtained from 198 out of 293 municipalities and from 518.635 animals, exclusively from SC. There was a statistically significant difference (P < 0.001) between the prevalence of F. hepatica and land altitude ( ρ ^ s = -0.43). The highest PI (above 10.1%) was observed in cities at 500 to 600 m (P < 0.01; ρ ^ s = -0.47) of altitude. There was no correlation between fascioliasis and rainfall in SC. It was determined that weather conditions in the past decade did not impose any limitation to the occurrence of the parasite, making it a disease of permanent clinical importance. These findings are essential to regions with similar geographical and climate conditions (i.e. altitude), when considering long-term control measurements, where animals and humans can be infected.

Correlation between climate data and land altitude for Fasciola hepatica infection in cattle in Santa Catarina, Brazil / Correlação entre clima e altitude para determinar a infecção de Fasciola hepatica em bovinos em Santa Catarina, Brasil

Abstract Fascioliasis is a food-borne parasitic disease that affects a range of animals, including humans caused by Fasciola hepatica. The present study aimed to determine the spatial distribution of bovine fasciolosis and to assess the correlation between the high Positivity Index (PI) and climate data and land altitude, from 2004 to 2008 and 2010 in Santa Catarina (SC), Brazil. Condemned livers of slaughtered animals were obtained from 198 out of 293 municipalities and from 518.635 animals, exclusively from SC. There was a statistically significant difference (P < 0.001) between the prevalence of F. hepatica and land altitude ( ρ ^ s = -0.43). The highest PI (above 10.1%) was observed in cities at 500 to 600 m (P < 0.01; ρ ^ s = -0.47) of altitude. There was no correlation between fascioliasis and rainfall in SC. It was determined that weather conditions in the past decade did not impose any limitation to the occurrence of the parasite, making it a disease of permanent clinical importance. These findings are essential to regions with similar geographical and climate conditions (i.e. altitude), when considering long-term control measurements, where animals and humans can be infected.

Resumo A fasciolose é uma doença parasitária que afeta uma gama de animais, incluindo humanos, causada por Fasciola hepatica no Brasil. Este estudo teve o objetivo de determinar a distribuição espacial da fasciolose e conferir a correlação do alto índice de positividade (PI), com os dados de clima e altitude, entre 2004 a 2008 e 2010 em Santa Catarina (SC), Brasil. Foram obtidos fígados em frigoríficos de SC, de 518.635 animais de 198 municípios, de um total de 293. Houve diferença estatística significativa (P < 0,001) entre a prevalência de F. hepatica e a altitude ( ρ ^ s = -0,43). O maior PI (acima de 10,1%) foi observado em municípios de 500 a 600 m (P < 0,01; ρ ^ s = -0,47) de altitude. Não foi observada correlação entre fígados parasitados e pluviosidade em SC. Foi observado que os dados climáticos na ultima década não apresentaram limitação para a ocorrência do parasito, fazendo com que o desafio clinico da infecção tenha sido permanente. Os dados são importantes para locais com condições geográficas e climáticas semelhantes (ex. altitude), para considerar medidas de controle a longo prazo, nas quais animais e humanos poderão ser infectados.

Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil.

Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years.

Land use/cover classification in the Brazilian Amazon using satellite images.

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil.

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R(2) = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R(2) = 0.97), 2 (R(2) = 0.60), 3 (R(2) = 0.63) and 4 (R(2) = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R² = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R² = 0.97), 2 (R² = 0.60), 3 (R² = 0.63) and 4 (R² = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

MAPSAR Image Simulation Based on L-band Polarimetric Data from the SAR-R99B Airborne Sensor (SIVAM System).

This paper describes the methodology applied to generate simulated multipolarized L-band SAR images of the MAPSAR (Multi-Application Purpose SAR) satellite from the airborne SAR R99B sensor (SIVAM System). MAPSAR is a feasibility study conducted by INPE (National Institute for Space Research) and DLR (German Aerospace Center) targeting a satellite L-band SAR innovative mission for assessment, management and monitoring of natural resources. Examples of simulated products and their applications are briefly discussed.