First detection of Edwardsiella ictaluri (Proteobacteria: Enterobacteriaceae) in wild Australian catfish

The bacterium Edwardsiella ictaluri is considered to be one of the most significant pathogens of farmed catfish in the United States of America and has also caused mortalities in farmed and wild fishes in many other parts of the world. E. ictaluri is not believed to be present in wild fish populations in Australia, although it has previously been detected in imported ornamental fishes held in quarantine facilities. In an attempt to confirm freedom from the bacterium in Australian native fishes, we undertook a risk-based survey of wild catfishes from 15 sites across northern Australia. E. ictaluri was detected by selective culturing, followed by DNA testing, in Wet Tropics tandan (Tandanus tropicanus) from the Tully River, at a prevalence of 0.40 (95% CI 0.21-0.61). The bacterium was not found in fishes sampled from any of the other 14 sites. This is the first report of E. ictaluri in wild fishes in Australia.

Assessing the delay to detection and the size of the outbreak at the time of detection of incursions of foot and mouth disease in Australia

The time delay to detection of an outbreak of an emergency animal disease directly affects the size of the outbreak at detection and the likelihood that the disease can be eradicated. This time delay is a direct function of the efficacy of the surveillance system in the country involved. Australia has recently completed a comprehensive review of its general surveillance system examining regional variation in both the behaviour of modelled outbreaks of foot and mouth disease and the likelihood that each outbreak will be detected and reported to government veterinary services. The size of the outbreak and the time delay from introduction to the point where 95% confidence of detection was reached showed significant (p < 0.05) regional variation with the more remote northern areas experiencing smaller outbreaks that are less likely to spread and less likely to be reported to government services than outbreaks in the more developed southern areas of Australia. Outbreaks in the more densely populated areas may take up to 43 days until a 95% confidence of detection is achieved and at that time, the outbreak may involve up to 53 farms.

Assessment of different surveillance systems for avian influenza in commercial poultry in Catalonia (North-Eastern Spain)

Compulsory surveillance programmes for avian influenza (AI) have been implemented in domestic poultry and wild birds in all the European Member States since 2005. The implementation of these programmes is complex and requires a close evaluation. A good indicator to assess their efficacy is the sensitivity (Se) of the surveillance system. In this study, the sensitivities for different sampling designs proposed by the Spanish authorities for the commercial poultry population of Catalonia were assessed, using the scenario tree model methodology. These samplings were stratified throughout the territory of Spain and took into account the species, the types of production and their specific risks. The probabilities of detecting infection at different prevalences at both individual and holding level were estimated. Furthermore, those subpopulations that contributed more to the Se of the system were identified. The model estimated that all the designs met the requirements of the European Commission. The probability of detecting AI circulating in Catalonian poultry did not change significantly when the within-holding design prevalence varied from 30% to 10%. In contrast, when the among-holding design prevalence decreased from 5% to 1%, the probability of detecting AI was drastically reduced. The sampling of duck and goose holdings, and to a lesser extent the sampling of turkey and game bird holdings, increased the Se substantially. The Se of passive surveillance in chickens for highly pathogenic avian influenza (HPAI) and low pathogenicity avian influenza (LPAI) were also assessed. The probability of the infected birds manifesting apparent clinical signs and the awareness of veterinarians and farmers had great influence on the probability of detecting AI. In order to increase the probability of an early detection of HPAI in chicken, the probability of performing AI specific tests when AI is suspected would need to be increased.

Alternative methods for computing the sensitivity of complex surveillance systems

Stochastic scenario trees are a new and popular method by which surveillance systems can be analyzed to demonstrate freedom from pests and disease. For multiple component systems-such as a combination of a serological survey and systematically collected observations-it can be difficult to represent the complete system in a tree because many branches are required to represent complex conditional relationships. Here we show that many of the branches of some scenario trees have identical outcomes and are therefore redundant. We demonstrate how to prune branches and derive compact representations of scenario trees using matrix algebra and Bayesian belief networks. The Bayesian network representation is particularly useful for calculation and exposition. It therefore provides a firm basis for arguing disease freedom in international forums.

Current value of historical and ongoing surveillance for disease freedom: surveillance for bovine Johne's disease in Western Australia

'Confidence' in freedom from disease is generally derived from multiple sources of varied surveillance information, and typically this surveillance evidence has been accumulated over time. In the state of Western Australia (WA) the main surveillance evidence supporting Free Zone status in the national bovine Johne's disease (BJD) program comprises periodic surveys and the ongoing clinical diagnostic system. This paper illustrates a simple approach to current valuation of historical surveillance information, based on the calculated sensitivity of the surveillance processes, the time elapsed since the data were accumulated, and the probability of new introduction of disease into the population during that elapsed time. Surveillance system components (SSCs) contributing to the overall sensitivity of the surveillance system were the clinical diagnostic system and periodic targeted surveys. Sensitivity of each component was estimated using a stochastic scenario tree model of the surveillance process as implemented. Probability of introduction of BJD into WA during each time period was estimated retrospectively from a stochastic import risk analysis model applied to actual cattle importation data. The probability that the WA cattle population was free from infection (at design prevalences of 0.2% of herds and 2% of animals within an infected herd) was estimated following each of 11 years, giving a median probability that the State was free of BJD (at these design prevalences) at the end of 2005 of 0.89. The meaning of this result is discussed.

Demonstrating freedom from disease using multiple complex data sources 2: case study--classical swine fever in Denmark

A method for quantitative evaluation of surveillance for disease freedom has been presented in the accompanying paper (Martin et al., 2007). This paper presents an application of the methods, using as an example surveillance for classical swine fever (CSF) in Denmark in 2005. A scenario tree model is presented for the abattoir-based serology component of the Danish CSF surveillance system, in which blood samples are collected in an ad hoc abattoir sampling process, from adult pigs originating in breeding herds in Denmark. The model incorporates effects of targeting (differential risk of seropositivity) associated with age and location (county), and disease clustering within herds. A surveillance time period of one month was used in the analysis. Records for the year 2005 were analysed, representing 25,332 samples from 3528 herds; all were negative for CSF-specific antibodies. Design prevalences of 0.1-1% of herds and 5% of animals within an infected herd were used. The estimated mean surveillance system component (SSC) sensitivities (probability that the SSC would give a positive outcome given the animals processed and that the country is infected at the design prevalences) per month were 0.18, 0.63 and 0.86, for among-herd design prevalences of 0.001, 0.005 and 0.01. The probabilities that the population was free from CSF at each of these design prevalences, after a year of accumulated negative surveillance data, were 0.91, 1.00 and 1.00. Targeting adults and herds from South Jutland was estimated to give approximately 1.9, 1.6 and 1.4 times the surveillance sensitivity of a proportionally representative sampling program for these three among-herd design prevalences.

Demonstrating freedom from disease using multiple complex data sources 1: a new methodology based on scenario trees

Current methods to demonstrate zone or country freedom from disease are based on either quantitative analysis of the results of structured representative surveys, or qualitative assessments of multiple sources of evidence (including complex non-representative sources). This paper presents a methodology for objective quantitative analysis of multiple complex data sources to support claims of freedom from disease. Stochastic scenario tree models are used to describe each component of a surveillance system (SSC), and used to estimate the sensitivity of each SSC. The process of building and analysing the models is described, as well as techniques to take into account any lack of independence between units at different levels within a SSC. The combination of sensitivity estimates from multiple SSCs into a single estimate for the entire surveillance system is also considered, again taking into account lack of independence between components. A sensitivity ratio is used to compare different components of a surveillance system. Finally, calculation of the probability of country freedom from the estimated sensitivity of the surveillance system is illustrated, incorporating the use and valuation of historical surveillance evidence.