Fasciolosis, caused by the trematode parasite Fasciola hepatica, is a multi-host parasitic disease affecting many countries worldwide. It is a well-recognized clinically and economically important disease of food producing animals such as cattle and sheep. In the UK, the incidence and distribution of fasciolosis has been increasing in the last decade while the timing of acute disease is becoming more variable and the season suitable for parasite development outside the mammalian host has been extended. Meanwhile control is proving increasingly difficult due to changing weather conditions, increased animal movements and developing anthelmintic resistance. Forecasting models have been around for a long time to aid health planning related to fasciolosis control, but studies identifying management related risk factors are limited. Moreover, the lack of information on the accuracy of meat inspection and available liver fluke diagnostic tests hinders effective monitoring of disease prevalence and treatment. So far, the evaluation of tests available for the diagnosis of the infection in cattle has mainly been carried out using gold standard approaches or under experimental settings, the limitations of which are well known. In cattle, the infection mainly manifests as a sub-clinical disease, resulting in indirect production losses, which are difficult to estimate. The lack of obvious clinical signs results in these losses commonly being attributed to other causes such as poor weather conditions or bad quality forage. This further undermines establishment of appropriate control strategies, as it is difficult to convince farmers to treat without demonstrating clear economic losses of sub-clinical disease. This project explores the value of slaughterhouse data in understanding the changing epidemiology of fasciolosis, identifying sustainable control measures and estimating the effect of infection on production parameters using data collected at one of the largest cattle and sheep abattoirs in Scotland. Data used in this study include; a) abattoir data routinely collected during 2013 and 2014, b) data collected during 3 periods of abattoir based sampling, c) data collected through administration of a management questionnaire and d) climatic and environmental data from various online sources. A Bayesian extension of the Hui Walter no gold standard model was used to estimate the diagnostic sensitivity and specificity of five diagnostic tests for fasciolosis in cattle, which were applied on 619 samples collected from the abattoir during three sampling periods; summer 2013, winter 2014 and autumn 2014. The results provided novel information on the performance of these tests in a naturally infected cattle population at different times of the year. Meat inspection was estimated to have a sensitivity of 0.68 (95% BCI 0.61-0.75) and a specificity of 0.88 (95% BCI 0.85-0.91). Accurate estimates of sensitivity and specificity will allow for routine abattoir liver inspection to be used as a tool for monitoring the epidemiology of F. hepatica as well as evaluating herd health planning. Linear regression modelling was used to estimate the delay in reaching slaughter weight in beef cattle infected with F. hepatica, accounting for other important factors such as weight, age, sex, breed and farm as a random effect. The model estimated that cattle classified as having fluke based on routine liver inspection had on average 10 (95% CI 9-12) days greater slaughter age, assuming an average carcass weight of 345 kg. Furthermore, estimates from a second model indicated that the increase in age at slaughter was more severe for higher fibrosis scores. More precisely, the increase in slaughter age was 34 (95% CI 11-57) days for fibrosis score of 1, 93 (95% CI 57-128) days for fibrosis score 2 and 78 (95% CI 30-125) days for fibrosis score 3. Similarly, in a third model comparing different burden categories with animals with no fluke burden, there was a 31 (95% CI 7-56) days increase in slaughter age for animals with 1 to 10 parasites and 77 (95% CI 32-124) days increase in animals with more than 10 parasites found in their livers. Lastly, a multi-variable mixed effects logistic regression model was built to estimate the association between climate, environmental, management and animal specific factors and the risk of an animal being infected by F. hepatica. Multiple imputation methodology was employed to deal with missing data arising from skipped questions in the questionnaire. Results of the regression model confirmed the importance of temperature, rainfall and cattle movements in increasing the risk for fasciolosis, while it indicated that the presence of deer can increase the risk of infection and that male cattle have a reduced risk of infection. Overall, this project has used slaughterhouse data to fill important knowledge gaps regarding F. hepatica infection in cattle. It has provided valuable information on the accuracy of routine abattoir meat inspection, as well as other diagnostic tests. It has also provided estimates of the effect of infection on the time cattle take to reach slaughter weight at different levels of infection and identified relevant risk factors related to the infection. In conclusion, knowledge of the effect of infection on slaughter age, as well as regional risk factors for F. hepatica infection, along with an improved use of abattoir inspection results in the evaluation of treatment strategies, can provide farmers and veterinarians with better incentives and tools to improve their herd health strategies and in the longer term help reduce the incidence of liver fluke in cattle.

The objectives of the research study are to review existing subnational credit rating methodologies and their applicability in the South African context, to develop the quantitative parts of credit rating methodologies for two provincial departments (Department of Health and Department of Education) that best predict future payment behaviour, to test the appropriateness of the proposed methodologies and to construct the datasets needed. The literature study includes background information regarding the uniqueness of South Africa’s provinces and credit rating methodologies in general. This is followed by information on subnational credit rating methodologies, including a review of existing subnational credit rating methodologies and an assessment of the applicability of the information provided in the South African context. Lastly, the applicable laws and regulations within the South African regulatory framework are provided. The knowledge gained from the literature study is applied to the data that have been collected to predict the two departments’ future payment behaviour. Linear regression modelling is used to identify the factors that best predict future payment behaviour and to assign weights to the identified factors in a scientific manner. The resulting payment behaviour models can be viewed as the quantitative part of the credit ratings. This is followed by a discussion on further investigations to improve the models. The developed models (both the simple and the advanced models) are tested with regard to prediction accuracies using RAG (Red, Amber or Green) statuses. This is followed by recommendations regarding future model usage that conclude that the department-specific models outperform the generic models in terms of prediction accuracies. / PhD (Risk analysis), North-West University, Potchefstroom Campus, 2015

The objectives of the research study are to review existing subnational credit rating methodologies and their applicability in the South African context, to develop the quantitative parts of credit rating methodologies for two provincial departments (Department of Health and Department of Education) that best predict future payment behaviour, to test the appropriateness of the proposed methodologies and to construct the datasets needed. The literature study includes background information regarding the uniqueness of South Africa’s provinces and credit rating methodologies in general. This is followed by information on subnational credit rating methodologies, including a review of existing subnational credit rating methodologies and an assessment of the applicability of the information provided in the South African context. Lastly, the applicable laws and regulations within the South African regulatory framework are provided. The knowledge gained from the literature study is applied to the data that have been collected to predict the two departments’ future payment behaviour. Linear regression modelling is used to identify the factors that best predict future payment behaviour and to assign weights to the identified factors in a scientific manner. The resulting payment behaviour models can be viewed as the quantitative part of the credit ratings. This is followed by a discussion on further investigations to improve the models. The developed models (both the simple and the advanced models) are tested with regard to prediction accuracies using RAG (Red, Amber or Green) statuses. This is followed by recommendations regarding future model usage that conclude that the department-specific models outperform the generic models in terms of prediction accuracies. / PhD (Risk analysis), North-West University, Potchefstroom Campus, 2015