Extrapolating incomplete animal population and surveillance data for use in national disease control : examples from Myanmar and New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Veterinary Epidemiology, School of Veterinary Science at Massey University, Manawatu, New Zealand
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Date
2019
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Massey University
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Abstract
National level databases of animal numbers, locations, and movements provide the essential
foundations for disease outbreak investigations, disease control, and disease preparedness
activities. These activities are particularly important for managing and mitigating the risks of high
impact exotic disease outbreaks like foot-and-mouth disease (FMD) as well as other economically
important endemic diseases, which can significantly impact international trade and food security.
However, many countries worldwide either lack national animal databases entirely or have multiple,
fragmented databases that provide an incomplete picture of animal demographics. Consequently,
there has been growing interest in developing novel methods to infer missing information on animal
populations from other data sources, to quantify the extent of missing information, and to
understand the impacts of missing information on the predictions made from national disease
simulation models. This thesis explores these issues in the context of an FMD free country (New
Zealand) as well as a country with endemic FMD (Myanmar).
In Chapter 3, regression models were used to predict farm-level animal populations in New Zealand
based on available data on farm type and location. When the results were compared against a
subset of validated animal population data, the predictions at the farm level were found to be
inaccurate especially for small-scale farms that keep animals for personal consumption or as a
hobby. These properties are of particular interest to animal health authorities as they have been
identified as at risk for exotic disease outbreaks. In Chapter 4, the impacts of having inaccurate herd
size data on the predictions made by an FMD disease spread simulation model were explored. The
results were analysed using cox proportional hazard models and logistic regression models, which
showed that simulations run using actual animal population data indicated different optimal control
strategies for FMD than models run with imperfect data and these effects differed by the region in
New Zealand where the hypothetical disease outbreak was seeded.
In Chapter 5, high-resolution local survey data and low-resolution national remote sensor data were
used alone and in combination to predict the location of FMD positive villages in Myanmar, which
were identified by serological sampling conducted as part of a large OIE funded research project in
2016. The performance of both random forest models and logistic regression models were explored
using training and testing data sets. Bovine populations and proximity to cattle markets were found
to be significant risk factors for FMD seropositivity and the logistic regression models performed as
well as machine learning techniques. Chapter 6 compared verbal reports of FMD outbreaks from village headman and householders against the serological test results from their villages to
determine whether using public reports is a viable alternative to conducting resource intensive
serological surveys for estimating FMD prevalence in Myanmar. Although village headmen proved to
be a better source of FMD reports compared to householders, the verbal reports were still not as
accurate as serological tests in an endemic situation where both sensitivity and specificity of
observing clinical signs can be complicated by endemic stability and concurrent outbreaks of other
diseases.
The work in both chapters 5 and 6 was carried out using data from activities of the Livestock
Breeding and Veterinary Department and the OIE and as such separate human ethics approval was
not required for the surveys described.
Chapter 7 addressed the issue of estimating the scale of missing data in a national database by
comparing intensively collected interview information with recorded movements at the farm level
for farms involved in New Zealand’s Mycoplasma bovis eradication programme. The results showed
that dairy farmers often failed to record almost half of high risk movements including leased bulls,
calves sent offsite for rearing, and adult cattle sent away for winter grazing. It was also estimated
that approximately 60% of animals arriving at abattoirs in New Zealand have multiple movements
missing from their life history in the National Animal Identification and Tracing system (NAIT)
database. This missing information had a significant impact on the ability of government and
industry to effectively respond to the outbreak. However, a positive finding from this study was that
the rates of missing data are decreasing over time.
Overall, this thesis demonstrated the importance of enhancing efforts to collect accurate and up-to-
date national animal population and movement data. For New Zealand, the changes required to
improve the national farm animal data landscape include improving compliance with the legislated
requirements to record animal movements and modifying the existing databases to record
information on the health status of animals against a unique animal identifier. A unique farm
identifier is required at the national level and should be agreed upon by industry representatives,
government and researchers. The combination of animal health data associated with the unique
animal identifier and a single current farm identifier for all farms will result in a robust animal health
and biosecurity system.
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Keywords
Livestock, Diseases, Burma, New Zealand, Prevention, Mathematical models, Monitoring