Optimising the use of new data streams for making epidemiological inferences in veterinary epidemiology : a thesis presented in partial fulfilment of the requirements for the degree of PhD in Veterinary Epidemiology at Massey University, Manawatu, New Zealand
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Date
2018
DOI
Open Access Location
Authors
Hidano, Arata
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Volume Title
Publisher
Massey University
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Abstract
Many ‘big data’ streams have recently become available in animal health
disciplines. While these data may be able to provide valuable epidemiological
information, researchers are at risk of making erroneous inferences if
limitations in these data are overlooked. This thesis focused on understanding
the better use of two data streams—livestock movement records and genetic
sequence data.
The first study analysed national dairy cattle movement data in New
Zealand to explore whether regionalisation of the country based on bovine
tuberculosis risk influenced trade decisions. The results suggested that the
observed livestock movement patterns could be explained by the majority of,
but not all, farmers avoiding purchasing cattle from high disease risk areas.
The second study took an alternative approach—qualitative interviews—to
understanding farmers’ livestock purchasing practices. This study suggested
that farmers are not necessarily concerned with disease status of source
farms and that it may be the reliance on stock agents to facilitate trade that
creates the observed livestock movement patterns in New Zealand. The
findings from this study also implied that various demographic and
production characteristics of animals may influence farmers’ livestock selling
practices, which were quantitatively verified in the third study analysing
livestock movement data and animal production data. These studies not only
showed that analyses based solely on ‘big data’ can be misleading but also
provided useful information necessary to predict future livestock movement
patterns. The final study evaluated the performance of various genetic
sequence sampling strategies in making phylodynamic inferences. We showed
that using all available genetic samples can be not only computationally
expensive, but also may lead to erroneous inferences. The results also
suggested that strategies for sampling genetic sequences for phylodynamic
analyses may need to be tailored based on epidemiological characteristics of
each epidemic.
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Keywords
Veterinary epidemiology, New Zealand, Data processing, Dairy cattle, Databases, Cattle trade, Tuberculosis in cattle, Genetics