Modelling the effects of ecology on wildlife disease surveillance

Surveillance is the first line of defence against disease, whether to monitor endemic cycles or to detect emergent epidemics. Knowledge of disease in wildlife is of considerable importance for managing risks to humans, livestock and wildlife species. Recent public health concerns (e.g. Highly Pathogenic Avian Influenza, West Nile Virus, Ebola) have increased interest in wildlife disease surveillance. However, current practice is based on protocols developed for livestock systems that do not account for the potentially large fluctuations in host population density and disease prevalence seen in wildlife. A generic stochastic modelling framework was developed where surveillance of wildlife disease systems are characterised in terms of key demographic, epidemiological and surveillance parameters. Discrete and continuous state-space representations respectively, are simulated using the Gillespie algorithm and numerical solution of stochastic differential equations. Mathematical analysis and these simulation tools are deployed to show that demographic fluctuations and stochasticity in transmission dynamics can reduce disease detection probabilities and lead to bias and reduced precision in the estimates of prevalence obtained from wildlife disease surveillance. This suggests that surveillance designs based on current practice may lead to underpowered studies and provide poor characterisations of the risks posed by disease in wildlife populations. By parameterising the framework for specific wildlife host species these generic conclusions are shown to be relevant to disease systems of current interest. The generic framework was extended to incorporate spatial heterogeneity. The impact of design on the ability of spatially distributed surveillance networks to detect emergent disease at a regional scale was then assessed. Results show dynamic spatial reallocation of a fixed level of surveillance effort led to more rapid detection of disease than static designs. This thesis has shown that spatio-temporal heterogeneities impact on the efficacy of surveillance and should therefore be considered when undertaking surveillance of wildlife disease systems.