Animal health surveillance is an important part of animal health care, particularly in
countries dependent on livestock for food production and international trade. There are
two major issues related to the provision of e®ective surveillance activities. Firstly, for
good information to become available, the design and conduct of data collection activ-
ities should be carried out following sound statistical principles. In reality, constraints
such as imperfect tests and unavoidably-biased sampling strategies hinder straightfor-
ward analysis and interpretation of survey results. Risk-based surveillance is used to
target high-risk sub-populations to increase e±ciency of disease detection; however,
biased datasets are generated.
This thesis develops methodologies to design risk-based surveillance systems and al-
low statistically valid analysis of the inherently biased data they generate. The ¯rst
example describes the development of a method to analyse surveillance data gathered
for bovine spongiform encephalopathy (BSE). The data are collected from four dif-
ferent surveillance streams of animals tested for BSE, with each stream containing
unavoidable biases and limitations. In the BSurvE model, these data are combined
with demographic information for each birth cohort to estimate the proportion of each
birth cohort infected with BSE. The prevalence of BSE in a national herd can then
be estimated using the method of moments, whereby the observed number of infected
animals is equated with the expected number. The upper 95% con¯dence limit for the
prevalence is estimated both for infected countries and for those where no BSE has
previously been detected.
A similar approach to that used in BSurvE is then applied to surveillance data for
trichinellosis, for which risk-based post-mortem testing is also performed. Negative
results from multiple species using di®erent, imperfect tests are combined to give an
estimate of the upper 95% con¯dence limit of the national prevalence of trichinellosis
in a reference population. This method is used to provide support for freedom from
trichinellosis in Great Britain.
A di®erent approach to risk-based surveillance is explored as the surveillance strategy
for detection of exotic causes of abortion in sheep and goats in New Zealand is examined.
Using a geographic information system (GIS) maps of disease risk factors were overlain
to produce a risk landscape for the lower North Island. This was used to demonstrate
how areas of high- and low-risk of disease occurrence can be identi¯ed and used to
guide the design of a risk-based surveillance programme.
Secondly, within one surveillance objective there may be many ways in which the
available funds or human resources could be distributed. This thesis develops a method
to assess BSE surveillance programmes, and provides tools to facilitate BSE detection
on the basis of infection risk and to increase the e±ciency of surveillance strategies.
A novel approach to allocation of resources is developed, where portfolio theory con-
cepts from ¯nance are applied to animal health surveillance. The example of surveil-
lance for exotic causes of sheep and goat abortion is expanded upon. Risk of disease
occurrence is assessed for a population over di®erent time periods and geographical
areas within a country, and portfolio theory used to allocate the number of tests to
be carried out within each of these boundaries. This method is shown to be more
likely to detect disease in a population when compared to proportional allocation of
the available resources.
The studies presented here show new approaches that allow better utilisation of
imperfect data and more e±cient use of available resources. They allow development of
surveillance programmes containing an appropriate balance of scanning and targeted
surveillance activities. Application of these methods will enhance the implementation
and value of surveillance in animal health.