This study aimed to evaluate strategies to enhance the early detection of foot and mouth disease incursions in Australia. Two strategies were considered. First, improving the performance of the current passive surveillance system. Second, supplementing the current passive system with active surveillance strategies based on testing animals at sale yards or through bulk milk testing of dairy herds. Simulation modelling estimated the impact of producer education and awareness by either increasing the daily probability that a farmer will report the presence of diseased animals or by reducing the proportion of the herd showing clinical signs required to trigger a disease report. Both increasing the probability of reporting and reducing the proportion of animals showing clinical signs resulted in incremental decreases in the time to detection, the size and the duration of the outbreak. A gold standard system in which all producers reported the presence of disease once 10% of the herd showed clinical signs reduced the median time to detection of the outbreak from 20 to 15 days, the duration of the subsequent outbreak from 53 to 42 days and the number of infected farms from 46 to 32. Bulk milk testing reduced the median time to detection by two days and the number of infected farms by six but had no impact on the duration of the outbreak. Screening of animals at sale yards provided no improvement over the current passive surveillance system alone while having significant resource issues. It is concluded that the most effective way to achieve early detection of incursions of foot and mouth disease into Victoria, Australia is to invest in improving producer reporting.

Vaccination is increasingly being recognised as a potential tool to supplement stamping out' for controlling foot-and-mouth disease (FMD) outbreaks in non-endemic countries. Infectious disease simulation models provide the opportunity to determine how vaccination might be used in the face of an FMD outbreak. Previously, consistent relative benefits of specific vaccination strategies across different FMD simulation modelling platforms have been demonstrated, using a UK FMD outbreak scenario. We extended this work to assess the relative effectiveness of selected vaccination strategies in five countries: Australia, New Zealand, the USA, the UK and Canada. A comparable, but not identical, FMD outbreak scenario was developed for each country with initial seeding of Pan Asia type O FMD virus into an area with a relatively high density of livestock farms. A series of vaccination strategies (in addition to stamping out (SO)) were selected to evaluate key areas of interest from a disease response perspective, including timing of vaccination, species considerations (e.g. vaccination of only those farms with cattle), risk area vaccination and resources available for vaccination. The study found that vaccination used with SO was effective in reducing epidemic size and duration in a severe outbreak situation. Early vaccination and unconstrained resources for vaccination consistently outperformed other strategies. Vaccination of only those farms with cattle produced comparable results, with some countries demonstrating that this could be as effective as all species vaccination. Restriction of vaccination to higher risk areas was less effective than other strategies. This study demonstrates consistency in the relative effectiveness of selected vaccination strategies under different outbreak start up conditions conditional on the assumption that each of the simulation models provide a realistic estimation of FMD virus spread. Preferred outbreak management approaches must however balance the principles identified in this study, working to clearly defined outbreak management objectives, while having a good understanding of logistic requirements and the socio-economic implications of different control measures.

Agent-based models (ABMs) are well suited to representing the spatiotemporal spread and control of disease in a population. The explicit modelling of individuals in a large population, however, can be computationally intensive, especially when models are stochastic and/or spatially-explicit. Large-scale ABMs often require a highly parallel platform such as a high-performance computing cluster, which tends to confine their utility to university, defence and scientific research environments. This poses a challenge for those interested in modelling the spread of disease on a large scale with access only to modest hardware platforms.The Australian Animal DISease (AADIS) model is a spatiotemporal ABM of livestock disease spread and control. The AADIS ABM is able to complete complex national-scale simulations of disease spread and control on a personal computer. Computational efficiency is achieved through a hybrid model architecture that embeds equation-based models inside herd agents, an asynchronous software architecture, and a grid-based spatial indexing scheme. (C) 2015 Elsevier Ltd. All rights reserved.

Nipah virus (NiV) is a recently emerged zoonotic virus that causes severe disease in humans. The reservoir hosts for NiV, bats of the genus Pteropus (known as flying-foxes) are found across the Asia-Pacific including Australia. While NiV has not been detected in Australia, evidence for NiV infection has been found in flying-foxes in some of Australia's closest neighbours. A qualitative risk assessment was undertaken to assess the risk of NiV establishing in Australian flying-foxes through flying-fox movements from nearby regions. Events surrounding the emergence of new diseases are typically uncertain and in this study an expert opinion workshop was used to address gaps in knowledge. Given the difficulties in combining expert opinion, five different combination methods were analysed to assess their influence on the risk outcome. Under the baseline scenario where the median was used to combine opinions, the risk was estimated to be very low. However, this risk increased when the mean and linear opinion pooling combination methods were used. This assessment highlights the effects that different methods for combining expert opinion have on final risk estimates and the caution needed when interpreting these outcomes given the high degree of uncertainty in expert opinion. This work has provided a flexible model framework for assessing the risk of NiV establishment in Australian flying-foxes through bat movements which can be updated when new data become available.

An outbreak of foot and mouth disease in Australia would trigger a major disease control and eradication program that would include restriction of movement of live animals within defined disease control zones. Experiences from outbreaks in other countries show that restrictions that limit the ability to turn off stock can lead to animal welfare compromise on intensively managed farms that are not infected with the disease. Intensive pig farms are considered to be at high risk of developing welfare problems during a control program due to the imposed movement restrictions and limited space available to house growing pigs. This study was designed to investigate strategies that could be used to mitigate animal welfare problems on intensive pig farms during a simulated outbreak of foot and mouth disease in a livestock dense region of Australia. Three strategies for managing farms affected by animal welfare problems were assessed, including on-farm culling of grower and finisher pigs, on-farm culling of finisher pigs only, and permit-based movement of finisher pigs to slaughter at abattoir. Under traditional approaches of giving infected premises (IP) priority over culling of farms with welfare problems (WP), delays of up to 25 days were experienced prior to culling of WPs. Deployment of vaccination did little to reduce the delay to culling of WPs. These delays were sensitive to resources available for control, with reduced resources increasing the time until welfare problems were addressed. Assigning equal priority to all farms requiring culling regardless of status as IP or WP and culling each as they arose reduced the delay to culling of WPs to no more than 4 days without large increases in either the duration or the size of the outbreaks observed.

An epidemic of exotic disease in a livestock population can lead to substantial economic losses.For example, the projected cost of a foot-and-mouth disease (FMD) epidemic in Australia is in the billions of dollars.This includes the direct cost of eradicating the disease (e.g., movement restrictions, culling and vaccination), and the impact to export markets from the loss of Australia's FMD-free status.Epidemics can be difficult to study empirically, particularly if a pathogen is dangerous, rare, or simply not present in a country.In these circumstances a model of disease spread can be a valuable epidemiological tool.When responding to an epidemic, animal health personnel might be restricted to enacting existing policies that leave little scope for the trialing of new control strategies.Computational modelling compensates for the limited opportunities an epidemiologist has to experiment in the field.Models of disease spread typically employ population-level approaches such as equation-based modelling, or individual-level approaches such as agent-based modelling.Population-level models can be concise and computationally efficient, but they do not isolate individual contributions to an epidemic.The finer granularity of individual-level models can introduce a computational overhead.In the case of a very largescale model, an individual-level approach can require a highly parallel platform such as a high-performance computing cluster in order to function efficiently.Epidemics are dynamically shaped by the complex interplay between host, pathogen and the environment.Modelling livestock disease spread on a national scale presents unique challenges due to large populations, varying herd types and farming practices, and regional and geopolitical differences.An alternative to pure population-level and individual-level modelling is a fusion of the two approaches into a hybrid model.This tactic is employed in the Australian Animal Disease Spread (AADIS) model, currently under development.The spread of disease within a herd is modelled from the top down by a system of ordinary differential equations.The spread of disease between herds is modelled from the bottom up by a spatially-aware agentbased model.Homogeneity is a reasonable abstraction for a herd of domestic animals and thus intra-herd spread of disease is well suited to equation-based modelling.The national set of herds is however, heterogeneous, making inter-herd spread of disease well suited to agent-based modelling.AADIS models the transfer of disease from an infectious herd to a susceptible herd by five stochastic spread pathways: direct contact, indirect contact, local spread, airborne transmission and spread through saleyards.Herds can be viewed abstractly as autonomous nodes in a network.Over discrete time steps of one day, the disease spread pathways generate the network topology.Network paths can subsequently be traversed forward to assess the downstream impact of an infected herd, or backward to trace the historical infection route.The network topology thus captures the spatiotemporal history of the simulated epidemic.AADIS is implemented in Java and employs open-source products such as PostgreSQL, PostGIS and OpenMap.It has an asynchronous object-oriented architecture that takes advantage of the inexpensive parallelism available on a multi-core x64 target.

An outbreak of foot and mouth disease (FMD) could seriously impact Australia's livestock sector and economy. As an FMD-free country, an outbreak would trigger a major disease control and eradication program that would include the culling of infected and at risk animals ('stamping out'), movement restrictions and zoo-sanitary measures. Additional control measures may also include pre-emptive culling or vaccination. However, it is unclear what disease strategy would be most effective under Australian conditions and different resource levels. Using a stochastic simulation model that describes FMD transmission between farms in a livestock dense region of Australia, our results suggest that using current estimates of human resource capacity for surveillance, infected premises operations and vaccination, outbreaks were effectively controlled under a stamping out strategy. However, under more constrained resource allocations, ring vaccination was more likely to achieve eradication faster than stamping out or pre-emptive culling strategies.

OBJECTIVE To provide an overview and descriptive analysis of the 2011 arboviral disease epidemic in horses that involved three important Australian mosquito-borne viruses: Murray Valley encephalitis virus, West Nile virus (Kunjin strain) and Ross River virus. METHODS Data from states affected between January and June 2011 were collated and comprised reports of horses showing signs of neuromuscular disease and the associated laboratory findings. A summary of the data is presented, together with a spatiotemporal analysis of cases and preliminary assessment of rainfall patterns and case distribution. RESULTS A total of 982 cases of equine arboviral disease were reported across Australia between January and June 2011. The majority of cases were reported from south-east Australia and included horses that developed neurological signs consistent with encephalitis. It was the largest epidemic of equine arboviral disease in Australia's history. Two likely causes for this unprecedented epidemic were the unusual weather events that preceded the epidemic and the emergence of a new strain of Kunjin virus. CONCLUSIONS The epidemic highlights to horse owners and policy makers the potential for future outbreaks of arboviral diseases and the need for vigilance. It also highlights the complex interactions among hosts, vectors and climatic conditions that are required for such an outbreak to occur.