Foot-and-mouth disease (FMD) is a highly contagious and economically important viral disease of cloven-hoofed animals. Australia's freedom from FMD underpins a valuable trade in live animals and animal products. An outbreak of FMD would result in the loss of export markets and cause severe disruption to domestic markets. The prevention of, and contingency planning for, FMD are of key importance to government, industry, producers and the community. The spread and control of FMD is complex and dynamic due to a highly contagious multi-host pathogen operating in a heterogeneous environment across multiple jurisdictions. Epidemiological modeling is increasingly being recognized as a valuable tool for investigating the spread of disease under different conditions and the effectiveness of control strategies. Models of infectious disease can be broadly classified as: population-based models that are formulated from the top-down and employ population-level relationships to describe individual-level behavior; individual-based models that are formulated from the bottom-up and aggregate individual-level behavior to reveal population-level relationships; and hybrid models which combine the two approaches into a single model. The Australian Animal Disease Spread (AADIS) hybrid model employs a deterministic equation-based model (EBM) to model within-herd spread of FMD, and a stochastic, spatially-explicit agent-based model (ABM) to model between-herd spread and control. The EBM provides concise and computationally efficient predictions of herd prevalence and clinical signs over time. The ABM captures the complex, stochastic and heterogeneous environment in which an FMD epidemic operates. The AADIS event-driven hybrid EBM/ABM architecture is a flexible, efficient and extensible framework for modeling the spread and control of disease in livestock on a national scale. We present an overview of the AADIS hybrid approach, a description of the model's epidemiological capabilities, and a sample case study comparing two strategies for the control of FMD that illustrates some of AADIS's functionality.
Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application.
Feral pig populations are expanding in many regions of the world following historically recent introductions. Populations are controlled to reduce damage to agriculture and the environment, and are also a recreational hunting resource. Knowledge of the area over which feral pigs may expand in the future could be used regionally to assist biosecurity planning, control efforts and the protection of biodiversity assets. The present study sought to estimate the future distribution of a recently introduced, expanding feral pig population in the remote Kimberley region of north-western Australia. An existing survey of feral pig distributions was enhanced and remote-sensing and weather data, reflecting or correlated with factors that may affect feral pig distributions, were collated and analysed. Relationships between feral pig distributions and these data were identified by using a generalised additive modelling approach. By the use of the model, the distribution of favourable habitat was estimated across the study region (89 125 km 2 ). The potential future distribution of feral pigs in the Kimberley was then estimated, assuming only natural dispersal of feral pigs from areas of known feral pig status (cf. hunter-assisted movements or escape of domestic pigs). The modelling suggests that feral pigs could expand their distribution by realistic natural dispersal in the future (to 61 950 km 2 ). This expansion possibility contains several strategically important areas (such as sea ports and biologically significant wetlands). This approach has the potential to improve biosecurity planning for the containment of the feral pig in the Kimberley and may have utility for other recently introduced invasive species in other regions. These results may also be used to improve pest-management programmes and contingency planning for exotic-disease incursions.
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