A retrospective study was carried from 2008 to 2013 to estimate the prevalence of liver flukes in ruminants slaughtered at the abattoir of Kermanshah province, west of Iran. A total of 663,633 animals slaughtered in the 5-year period and totally 1.95 % of livers were condemned due to liver flukes. Fasciolosis were responsible for 0.8, 0.7 and 1.5 % of liver condemnations, whereas 1, 0.8 and 1 % of liver were condemned because of Dicrocoelium dendriticum infection in the sheep, goats and cattle, respectively. A significant difference in the prevalence of liver flukes were observed among studied animals (p \ 0.001) and the highest and lowest prevalence were detected in cattle and goats, respectively. Data showed significant seasonal pattern for distomatosis in sheep and goat (p \ 0.001). Liver condemnations due to fasciolosis were prevalent in sheep and goats and cattle slaughtered during winter, summer and autumn, respectively, whereas dicrocoeliosis were common in autumn season for sheep and cattle and in winter for goats. This survey provides baseline data for the future monitoring of these potentially important parasitic infections in the region.
A retrospective study was carried out from 2008 to 2013 to estimate the prevalence of hydatidosis in ruminants slaughtered at the Kermanshah municipal abattoir, in western Iran. A total number of 663,633 livestock (393,585 sheep, 81,080 goats and 188,968 cattle) slaughtered in the 5-year period and overall 9,524 (1.43 %) livers and 13,147 (1.98 %) lungs were condemned. The lungs were more frequently infected with hydatid cysts than the livers in all animal species. The average prevalence of hydatidosis was 2.7 % in this area. The prevalence of Echinococcus granulosus infection recorded in the present study was generally lower than those reported from other regions of Iran. Greater awareness among farmers, destruction of organs containing hydatid cysts, prevention of access of dogs to raw offals and implementation of national rabies control program could be responsible factors. The results showed a significant difference (p < 0.001) in the prevalence of hydatidosis among studied animals with higher prevalence in cattle than sheep, with the lowest prevalence recorded in goats. However the annual prevalence of liver and lung condemnations due to hydatidosis was decreased in some years, but the overall trend had a variable pattern in the prevalence of hydatidosis over the study period. Data showed a significant seasonal pattern for hydatidosis in all studied animals. Liver and lung condemnations due to hydatidosis were higher in the fall for sheep and cattle, whereas in goats were higher in summer. This could be attributed to various factors such as sources of slaughtered animals, changes in management practice and ecological factors. The current results suggest that a systematic investigation that lead to a disease control strategy is required to reduce the economic and public health consequences of hydatidosis. In addition, the present survey provides baseline data for the future monitoring of this potentially important parasitic disease in the region.
Lumpy skin disease virus (LSDV) causes an infectious disease in cattle. Due to its direct relationship with the survival of arthropod vectors, geospatial and climatic features play a vital role in the epidemiology of the disease. The objective of this study was to assess the ability of some machine learning algorithms to forecast the occurrence of LSDV infection based on meteorological and geological attributes. Initially, ExtraTreesClassifier algorithm was used to select the important predictive features in forecasting the disease occurrence in unseen (test) data among meteorological, animal population density, dominant land cover, and elevation attributes. Some machine learning techniques revealed high accuracy in predicting the LSDV occurrence in test data (up to 97%). In terms of area under curve (AUC) and F1 performance metric scores, the artificial neural network (ANN) algorithm outperformed other machine learning methods in predicting the occurrence of LSDV infection in unseen data with the corresponding values of 0.97 and 0.94, respectively. Using this algorithm, the model consisted of all predictive features and the one which only included meteorological attributes as important features showed similar predictive performance. According to the findings of this research, ANN can be used to forecast the occurrence of LSDV infection with high precision using geospatial and meteorological parameters. Applying the forecasting power of these methods could be a great help in conducting screening and awareness programs, as well as taking preventive measures like vaccination in areas where the occurrence of LSDV infection is a high risk.
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