A Simulation Study of the Use of Vaccination to Control Foot-and-Mouth Disease Outbreaks Across Australia.

This study examines the potential for foot-and-mouth disease (FMD) control strategies that incorporate vaccination to manage FMD spread for a range of incursion scenarios across Australia. Stakeholder consultation was used to formulate control strategies and incursion scenarios to ensure relevance to the diverse range of Australian livestock production regions and management systems. The Australian Animal Disease Spread model (AADIS) was used to compare nine control strategies for 13 incursion scenarios, including seven control strategies incorporating vaccination. The control strategies with vaccination differed in terms of their approaches for targeting areas and species. These strategies are compared with two benchmark strategies based on stamping out only. Outbreak size and duration were compared in terms of the total number of infected premises, the duration of the control stage of an FMD outbreak, and the number of vaccinated animals. The three key findings from this analysis are as follows: (1) smaller outbreaks can be effectively managed by stamping out without vaccination, (2) the size and duration of larger outbreaks can be significantly reduced when vaccination is used, and (3) different vaccination strategies produced similar reductions in the size and duration of an outbreak, but the number of animals vaccinated varied. Under current international standards for regaining FMD-free status, vaccinated animals need to be removed from the population at the end of the outbreak to minimize trade impacts. We have shown that selective, targeted vaccination strategies could achieve effective FMD control while significantly reducing the number of animals vaccinated.

Early Decision Indicators for Foot-and-Mouth Disease Outbreaks in Non-Endemic Countries.

Disease managers face many challenges when deciding on the most effective control strategy to manage an outbreak of foot-and-mouth disease (FMD). Decisions have to be made under conditions of uncertainty and where the situation is continually evolving. In addition, resources for control are often limited. A modeling study was carried out to identify characteristics measurable during the early phase of a FMD outbreak that might be useful as predictors of the total number of infected places, outbreak duration, and the total area under control (AUC). The study involved two modeling platforms in two countries (Australia and New Zealand) and encompassed a large number of incursion scenarios. Linear regression, classification and regression tree, and boosted regression tree analyses were used to quantify the predictive value of a set of parameters on three outcome variables of interest: the total number of infected places, outbreak duration, and the total AUC. The number of infected premises (IPs), number of pending culls, AUC, estimated dissemination ratio, and cattle density around the index herd at days 7, 14, and 21 following first detection were associated with each of the outcome variables. Regression models for the size of the AUC had the highest predictive value (R2 = 0.51-0.9) followed by the number of IPs (R2 = 0.3-0.75) and outbreak duration (R2 = 0.28-0.57). Predictability improved at later time points in the outbreak. Predictive regression models using various cut-points at day 14 to define small and large outbreaks had positive predictive values of 0.85-0.98 and negative predictive values of 0.52-0.91, with 79-97% of outbreaks correctly classified. On the strict assumption that each of the simulation models used in this study provide a realistic indication of the spread of FMD in animal populations. Our conclusion is that relatively simple metrics available early in a control program can be used to indicate the likely magnitude of an FMD outbreak under Australian and New Zealand conditions.

Identifying areas of Australia at risk for H5N1 avian influenza infection from exposure to nomadic waterfowl moving throughout the Australo-Papuan region.

Since 2003, highly pathogenic avian influenza (HPAI) due to the H5N1 virus has been reported from both domestic poultry and wild birds in over 60 countries and this has resulted in the direct death or slaughter of over 250 million birds. The potential for HPAI to be introduced to Australian commercial poultry via migratory shorebirds returning from Asia has previously been assessed as a low risk. However, introduction of HPAI from areas to the immediate north of Australia via nomadic waterfowl that range throughout the Australo-Papuan region provides a second potential pathway of entry. Surveillance programmes provide an important early warning for Australia's estimated 2,000 commercial poultry farms but to be efficient they should be risk-based and target resources at those areas and sectors of the industry at higher risk of exposure. In order to address this need, this study compared the distribution and movement patterns of native waterfowl to identify regions where the likelihood of HPAI incursion and establishment was highest. Analysis of bird banding records provided information on the maximum distances moved and dispersal patterns of the species of waterfowl of interest. Introduction via Cape York was found to be most likely and all poultry farms in Queensland were found to be within range of waterfowl that can shed H5N1 virus for up to 17 days. The final analysis showed that the area at greatest risk of HPAI introduction is the Atherton tableland near Cairns.

Early warning of Ross River virus epidemics: combining surveillance data on climate and mosquitoes.

BACKGROUND: Ross River virus disease is spread by mosquitoes, and an average of 5000 people are infected each year in Australia. It is one of the few infectious diseases for which climate-based early warning systems could be developed. The aim of this study was to test whether supplementing routinely collected climate data with mosquito surveillance data could increase the accuracy of disease prediction models. METHODS: We focused on a temperate region of Western Australia between July 1991 and June 1999. We developed "early" and "later" warning logistic regression models to test the sensitivity of data on climate (tide height, rainfall, sea surface temperature) and mosquito counts for predicting epidemics of disease. RESULTS: Climate data on their own were moderately sensitive (64%) for predicting epidemics during the early warning period. Addition of mosquito surveillance data increased the sensitivity of the early warning model to 90%. The later warning model had a sensitivity of 85%. CONCLUSIONS: We found that climate data are inexpensive and easy to collect and allow the prediction of Ross River virus disease epidemics within the time necessary to improve the effectiveness of public health responses. Mosquito surveillance data provide a more expensive early warning but add substantial predictive value.

Predicting Ross River virus epidemics from regional weather data.

BACKGROUND: Diseases caused by arboviruses cause extensive mortality and morbidity throughout the world. Weather directly affects the breeding, abundance, and survival of mosquitoes, the principal vector of many arboviruses. The goal of this study was to test whether climate variables could predict with high levels of accuracy (more than 70%) epidemics of one arbovirus, Ross River virus disease. METHODS: Weather data from two regions in southeastern Australia were matched with Ross River virus disease data for the period 1991 to 1999. Our aim was to develop simple models for the probability of the occurrence of an epidemic in an area in a given year. RESULTS: Two predictable epidemic patterns emerged, after either high summer rainfalls or high winter rainfalls. A prerequisite relating to host-virus dynamics was lower than average spring rainfall in the preepidemic year. The sensitivity of the model was 96% for Region 1 and 73% for Region 2. CONCLUSIONS: Early warning of weather conditions conducive to outbreaks of Ross River virus disease is possible at the regional level with a high degree of accuracy. Our models may have application as a decision tool for health authorities to use in risk-management planning.