Integrated modelling and Bayesian inference applied to population and disease dynamics in wildlife: M.Bovis in badgers in Woodchester Park
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social spatial chapter.zip (1.300Mb)
demography chapter.zip (3.566Mb)
disease chapter.zip (684.6Kb)
Date
29/06/2013Author
Zijerveld, Leonardus Jacobus Johannes
Metadata
Abstract
Understanding demographic and disease processes in wildlife populations tends
to be hampered by incomplete observations which can include significant errors.
Models provide useful insights into the potential impacts of key processes and the
value of such models greatly improves through integration with available data in
a way that includes all sources of stochasticity and error. To date, the impact
on disease of spatial and social structures observed in wildlife populations has
not been widely addressed in modelling. I model the joint effects of differential
fecundity and spatial heterogeneity on demography and disease dynamics, using a
stochastic description of births, deaths, social-geographic migration, and disease
transmission. A small set of rules governs the rates of births and movements in an
environment where individuals compete for improved fecundity. This results in
realistic population structures which, depending on the mode of disease transmission
can have a profound effect on disease persistence and therefore has an impact
on disease control strategies in wildlife populations. I also apply a simple model
with births, deaths and disease events to the long-term observations of TB (Mycobacterium
bovis) in badgers in Woodchester Park. The model is a continuous
time, discrete state space Markov chain and is fitted to the data using an implementation
of Bayesian parameter inference with an event-based likelihood. This
provides a flexible framework to combine data with expert knowledge (in terms of
model structure and prior distributions of parameters) and allows us to quantify
the model parameters and their uncertainties. Ecological observations tend to be
restricted in terms of scope and spatial temporal coverage and estimates are also
affected by trapping efficiency and disease test sensitivity. My method accounts
for such limitations as well as the stochastic nature of the processes. I extend
the likelihood function by including an error term that depends on the difference
between observed and inferred state space variables. I also demonstrate that the
estimates improve by increasing observation frequency, combining the likelihood
of more than one group and including variation of parameter values through the
application of hierarchical priors.
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