Recent developments in control of highly infectious diseases attempt to improve emergency response efforts by more clearly focusing or targeting response tools according to risk. For example, advances in surveillance testing and sampling deliver their results by more accurately and precisely targeting the population of interest. In this work, targeted implementation of trading zones and vaccination were examined for simulated outbreaks of foot-and-mouth disease (FMD) in Australia. Trading zones allowing unaffected Australian states to resume exports following an outbreak of FMD were assessed using multiple tools. A Victorian incursion scenario with traditional stamping out and vaccination as control options, was simulated using the AADIS model Version 2.47, to characterise the geographic extent of potential outbreaks, the number of animals infected, and the date of last cull indicating duration of the outbreak. Information on disease spread from the AADIS simulations was then used to identify the boundaries of trading zones for the incursion scenario, in which vaccination with trading zones was found to further reduce disease impacts relative to stamping out alone with trading zones. The number of animals culled due to disease provided supply shocks for stamping out alone and vaccinate-to-retain, while the number of vaccinated animals was added to the number of animals culled due to disease for the supply shock of vaccinate-to-remove. The day of last cull was combined with historical FMD trade recovery and Australian export data to estimate the share of Australian exports that would be embargoed under trading zones. The market impacts - changes in equilibrium quantities and prices - of the supply shock, trading zones, and consumer reactions - were simulated within ABARES' AgEmissions partial equilibrium model of Australian agriculture. For this simulated large outbreak, where vaccinate-to-remove was utilised along with trading zones, producer losses were reduced by AUD 4 billion in present value terms over 10 years estimated at a 7% discount rate (PV10,7%) compared to an outbreak where stamping out alone is applied with trading zones. Introducing FMD virus risk mitigation measures for wool to further target trading zones reduced the economic impacts by an additional AUD 3.6 billion (PV10,7%). Outbreak response cost savings and additional potential costs under vaccinate-to-retain with trading zones were also compared to the vaccinate-to-remove control with trading zones. Results emphasised the importance of outbreak characteristics in determining trading zones and targeting of vaccination. Economic analyses identified how additional investments in targeting outbreak response is of value to producers.

Epidemiological models of notifiable livestock disease are typically framed at a national level and targeted for specific diseases. There are inherent difficulties in extending models beyond national borders as details of the livestock population, production systems and marketing systems of neighbouring countries are not always readily available. It can also be a challenge to capture heterogeneities in production systems, control policies, and response resourcing across multiple countries, in a single transboundary model. In this paper, we describe EuFMDiS, a continental-scale modelling framework for transboundary animal disease, specifically designed to support emergency animal disease planning in Europe. EuFMDiS simulates the spread of livestock disease within and between countries and allows control policies to be enacted and resourced on a per-country basis. It provides a sophisticated decision support tool that can be used to look at the risk of disease introduction, establishment and spread; control approaches in terms of effectiveness and costs; resource management; and post-outbreak management issues.

Whilst emergency vaccination may help contain foot-and-mouth disease in a previously FMD-free country, its use complicates post-outbreak surveillance and the recovery of FMD-free status. A structured surveillance program is required that can distinguish between vaccinated and residually infected animals, and provide statistical confidence that the virus is no longer circulating in previously infected areas. Epidemiological models have been well-used to investigate the potential benefits of emergency vaccination during a control progam and when/where/whom to vaccinate in the face of finite supplies of vaccine and personnel. Less well studied are post-outbreak issues such as the management of vaccinated animals and the implications of having used vaccination during surveillance regimes to support proof-of-freedom. This paper presents enhancements to the Australian Animal Disease Model (AADIS) that allow comparisons of different post-outbreak surveillance sampling regimes for establishing proof-of-freedom from FMD. A case study is provided that compares a baseline surveillance sampling regime (derived from current OIE guidelines), with an alternative less intensive sampling regime. It was found that when vaccination was not part of the control program, a reduced sampling intensity significantly reduced the number of samples collected and the cost of the post-outbreak surveillance program, without increasing the risk of missing residual infected herds.

Following an FMD eradication program, surveillance will be required to demonstrate that the program has been successful. The World Organization for Animal Health (OIE) provides guidelines including waiting periods and appropriate surveillance to support regaining FMD-free status. Serological surveillance is the recommended method for demonstrating freedom but is time consuming and expensive. New technologies such as real-time reverse transcription polymerase chain reaction (RT-qPCR) tests and sampling techniques such as bulk milk testing (BMT) of dairy cattle, oral swabs, and saliva collection with rope tethers in piggeries could enable surveillance to be done more efficiently. Epidemiological modelling was used to simulate FMD outbreaks, with and without emergency vaccination as part of the response, in Australia. Baseline post-outbreak surveillance approaches for unvaccinated and vaccinated animals based on the European FMD directive were compared with alternative approaches in which the sampling regime, sampling approaches and/or the diagnostic tests used were varied. The approaches were compared in terms of the resources required, time taken, cost, and effectiveness i.e., ability of the surveillance regime to correctly identify the infection status of herds. In the non-vaccination scenarios, the alternative approach took less time to complete and cost less, with the greatest benefits seen with larger outbreaks. In vaccinated populations, the alternative surveillance approaches significantly reduced the number of herds sampled, the total number of tests done and costs of the post-outbreak surveillance. There was no reduction in effectiveness using the alternative approaches, with one of the benefits being a reduction in the number of false positive herds. Alternative approaches to FMD surveillance based on non-invasive sampling methods and RT-qPCR tests have the potential to enable post outbreak surveillance substantiating FMD freedom to be done more quickly and less expensively than traditional approaches based on serological surveys.

Decisions surrounding the presence of infectious diseases are typically made in the face of considerable uncertainty. However, the development of models to guide these decisions has been substantially constrained by computational difficulty. This paper focuses on the case of finding the optimal level of surveillance against a highly infectious animal disease where time, space and randomness are fully considered. We apply the Sample Average Approximation approach to solve our problem, and to control model dimension, we propose the use of an infection tree model, in combination with sensible ‘tree-pruning’ and parallel processing techniques. Our proposed model and techniques are generally applicable to a number of disease types, but we demonstrate the approach by solving for optimal surveillance levels against foot-and-mouth disease using bulk milk testing as an active surveillance protocol, during an epidemic, among 42,279 farms, fully characterised by their location, livestock type and size, in the state of Victoria, Australia.

Disease reporting is an essential frontline component of surveillance systems, particularly for detecting incursions of new and emerging diseases. It has the advantages of being comprehensive and continuous, with the potential to reduce the time of disease detection and the extent of consequent spread. A number of exotic diseases, including sheep and goat pox, lumpy skin disease, peste des petits ruminants and foot and mouth disease have historically entered into south-eastern Europe through the Thrace region, which extends across neighbouring areas of Greece, Bulgaria and Turkey. In this high-risk area, multiple factors can reduce the sensitivity of disease reporting across the diverse production systems and animal health services need robust and effective disease reporting systems. While describing a training exercise designed to provide animal health services of the three countries with the knowledge and skills for conducting comprehensive in-country assessments, we provide an initial evaluation of the sensitivity of foot and mouth disease reporting and identify gaps and constraints in the Thrace region. An expert elicitation approach was used to consult official veterinarians from central and local animal health authorities of the three countries, and scenario trees modelling was applied to analyse the collected data. The reported sensitivity of disease reporting often varied between the central and local veterinary authorities within the three countries. Awareness of clinical disease, of reporting procedures and of biosecurity measures affected the early stages of disease reporting, particularly in the production systems identified at lower reporting sensitivity such as small ruminant's herds, mixed bovine herds and backyard herds. Despite its limitations this training exercise provided an effective framework (a) to develop capacities of the veterinary services in the region and (b) to supply initial evidence for guiding further interventions targeting those sectors and stakeholders at lower reporting sensitivity to reduce risks of disease introduction.

An incursion of Foot-and-mouth disease (FMD) in a previously FMD-free country can cause significant economic damage from immediate and prolonged closure of FMD-sensitive markets. Whilst emergency vaccination may help contain disease, the presence of vaccinated animals complicates post-outbreak management and the recovery of FMD-free status for return to trade. We present enhancements to the Australian Animal DISease (AADIS) model that allow comparisons of post-outbreak management strategies for vaccinated animals, for the purposes of securing the earliest possible return to trade. Two case studies are provided that compare the retention of vaccinated animals with removal for waste/salvage, and the impact on recovery of FMD-sensitive markets per OIE guidelines. It was found that a vaccinate-and-retain strategy was associated with lower post-outbreak management costs, however this advantage was outweighed by significantly higher trade losses. Under the assumptions of the study there was no cost advantage to salvaging the removed vaccinated animals.

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

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.

Previous foot-and-mouth disease (FMD) outbreaks and simulation-based analyses suggest substantial payoffs from detecting an incursion early. However, no economic measures for early detection have been analysed in an optimising framework. We investigate the use of bulk milk testing (BMT) for active surveillance against an FMD incursion in Australia. We find that BMT can be justified, but only when the FMD entry probability is sufficiently high or the cost of BMT is low. However, BMT is well suited for post-outbreak surveillance, to shorten the length of time and size of an epidemic and to facilitate an earlier return to market.

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 modelling 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 behaviour, individual-based models that are formulated from the bottom-up and aggregate individual-level behaviour to reveal population-level relationships, or 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 modelling the spread and control of disease in livestock on a national scale. We present an overview of the AADIS hybrid approach and a description of the model's epidemiological capabilities

Early disease detection and efficient methods of proving disease freedom can substantially improve the response to incursions of important transboundary animal diseases in previously free regions. We used a spatially explicit, stochastic disease spread model to simulate the spread of classical swine fever in wild pigs in a remote region of northern Australia and to assess the performance of disease surveillance strategies to detect infection at different time points and to delineate the size of the resulting outbreak. Although disease would likely be detected, simple random sampling was suboptimal. Radial and leapfrog sampling improved the effectiveness of surveillance at various stages of the simulated disease incursion. This work indicates that at earlier stages, radial sampling can reduce epidemic length and achieve faster outbreak delineation and control, but at later stages leapfrog sampling will outperform radial sampling in relation to supporting faster disease control with a less-extensive outbreak area. Due to the complexity of wildlife population dynamics and group behaviour, a targeted approach to surveillance needs to be implemented for the efficient use of resources and time. Using a more situation-based surveillance approach and accounting for disease distribution and the time period over which an epidemic has occurred is the best way to approach the selection of an appropriate surveillance strategy.

Although wild pig populations are known to sometimes be infected by Salmonella, the situation in Australia has received little attention and few population-based, planned studies have been conducted. Understanding the distribution of Salmonella infections within wild pig populations allows the potential hazard posed to co-grazing livestock to be assessed. We sampled a remote and isolated wild pig population in northwestern Australia. Faecal and mesenteric lymph node samples were collected from 651 wild pigs at 93 locations and cultured for Salmonella. The population sampled was typical of wild pig populations in tropical areas of Australia, and sampling occurred approximately halfway through the population's breeding season (38% of the 229 adult females were pregnant and 35% were lactating). Overall, the prevalence of Salmonella infection based on culture of 546 freshly collected faecal samples was 36.3% (95% CI 32.1-40.7%), and based on culture of mesenteric lymph nodes was 11.9% (95% CI, 9.4-15.0%). A total of 39 serovars (139 isolates) were identified--29 in faecal samples and 24 in lymph node samples--however neither Salmonella enterica serovar Typhimurium nor Salmonella Cholerasuis were isolated. There was a significant (p<0.0001) disagreement between faecal and lymph node samples with respect to Salmonella isolation, with isolation more likely from faecal samples. Prevalence differed between age classes, with piglets being less likely to be faecal-positive but more likely to be lymph node positive than adults. The distribution of faecal-positive pigs was spatially structured, with spatial clusters being identified. Study results suggest that this population of wild pigs is highly endemic for Salmonella, and that Salmonella is transmitted from older to younger pigs, perhaps associated with landscape features such as water features. This might have implications for infection of co-grazing livestock within this environment.

Indonesia continues to report the highest number of human and poultry cases of highly pathogenic avian influenza H5N1. The disease is considered to be endemic on the island of Bali. Live bird markets are integral in the poultry supply chain on Bali and are important, nutritionally and culturally, for the rural and urban human populations. Due to the lack of biosecurity practiced along the supply chain from producer to live bird markets, there is a need to understand the risks associated with the spread of H5N1 through live bird movements for effective control. Resources to control H5N1 in Indonesia are very limited and cost effective strategies are needed. We assessed the probability a live bird market is infected through live poultry movements and assessed the effects of implementing two simple and low cost control measures on this risk. Results suggest there is a high risk a live bird market is infected (0.78), and risk mitigation strategies such as detecting and removing infected poultry from markets reduce this risk somewhat (range 0.67-0.76). The study demonstrates the key role live poultry movements play in transmitting H5N1 and the need to implement a variety of control measures to reduce disease spread.

Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.

Disease modelling is one approach for providing new insights into wildlife disease epidemiology. This paper describes a spatio-temporal, stochastic, susceptible- exposed-infected-recovered process model that simulates the potential spread of classical swine fever through a documented, large and free living wild pig population following a simulated incursion. The study area (300 000 km2) was in northern Australia. Published data on wild pig ecology from Australia, and international Classical Swine Fever data was used to parameterise the model. Sensitivity analyses revealed that herd density (best estimate 1-3 pigs km-2), daily herd movement distances (best estimate approximately 1 km), probability of infection transmission between herds (best estimate 0.75) and disease related herd mortality (best estimate 42%) were highly influential on epidemic size but that extraordinary movements of pigs and the yearly home range size of a pig herd were not. CSF generally established (98% of simulations) following a single point introduction. CSF spread at approximately 9 km2 per day with low incidence rates (< 2 herds per day) in an epidemic wave along contiguous habitat for several years, before dying out (when the epidemic arrived at the end of a contiguous sub-population or at a low density wild pig area). The low incidence rate indicates that surveillance for wildlife disease epidemics caused by short lived infections will be most efficient when surveillance is based on detection and investigation of clinical events, although this may not always be practical. Epidemics could be contained and eradicated with culling (aerial shooting) or vaccination when these were adequately implemented. It was apparent that the spatial structure, ecology and behaviour of wild populations must be accounted for during disease management in wildlife. An important finding was that it may only be necessary to cull or vaccinate relatively small proportions of a population to successfully contain and eradicate some wildlife disease epidemics.

Infectious wildlife diseases have enormous global impacts, leading to human pandemics, global biodiversity declines and socio-economic hardship. Understanding how infection persists and is transmitted in wildlife is critical for managing diseases, but our understanding is limited. Our study aim was to better understand how infectious disease persists in wildlife populations by integrating genetics, ecology and epidemiology approaches. Specifically, we aimed to determine whether environmental or host factors were stronger drivers of Salmonella persistence or transmission within a remote and isolated wild pig (Sus scrofa) population. We determined the Salmonella infection status of wild pigs. Salmonella isolates were genotyped and a range of data was collected on putative risk factors for Salmonella transmission. We a priori identified several plausible biological hypotheses for Salmonella prevalence (cross sectional study design) versus transmission (molecular case series study design) and fit the data to these models. There were 543 wild pig Salmonella observations, sampled at 93 unique locations. Salmonella prevalence was 41% (95% confidence interval [CI]: 37-45%). The median Salmonella DICE coefficient (or Salmonella genetic similarity) was 52% (interquartile range [IQR]: 42-62%). Using the traditional cross sectional prevalence study design, the only supported model was based on the hypothesis that abundance of available ecological resources determines Salmonella prevalence in wild pigs. In the molecular study design, spatial proximity and herd membership as well as some individual risk factors (sex, condition score and relative density) determined transmission between pigs. Traditional cross sectional surveys and molecular epidemiological approaches are complementary and together can enhance understanding of disease ecology: abundance of ecological resources critical for wildlife influences Salmonella prevalence, whereas Salmonella transmission is driven by local spatial, social, density and individual factors, rather than resources. This enhanced understanding has implications for the control of diseases in wildlife populations. Attempts to manage wildlife disease using simplistic density approaches do not acknowledge the complexity of disease ecology.

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.