The serological diagnosis of avian influenza (AI) can be performed using different methods, yet the haemagglutination inhibition (HI) test is considered the 'gold standard' for AI antibody subtyping. Although alternative diagnostic assays have been developed, in most cases, their accuracy has been evaluated in comparison with HI test results, whose performance for poultry has not been properly evaluated. The objective of this study was to estimate the diagnostic sensitivity (Se) and specificity (Sp) of the HI test and six other diagnostic assays for the detection of AI antibodies without assuming a gold standard. We applied a Bayesian version of latent class analysis to compare the results of multiple tests from different study settings reported in the literature. The results showed that the HI test has nearly perfect accuracy (i.e. 98·8% sensitivity and 99·5% specificity). It performed well in both chickens and turkeys and yet was less accurate in experimentally infected poultry, compared to naturally infected. Blocking ELISA and the indirect immunofluorescence assay also performed very well. Given its very high Se and Sp, the HI test may be effectively considered a gold standard. In the framework of LPAI surveillance, where large numbers of samples have to be processed, the blocking ELISA could be a valid alternative to the HI test, in that it is almost as sensitive and specific as the HI test yet quicker and easier to automate.

In recent years, the early detection of low pathogenicity avian influenza (LPAI) viruses in poultry has become increasingly important, given their potential to mutate into highly pathogenic viruses. However, evaluations of LPAI surveillance have mainly focused on prevalence and not on the ability to act as an early warning system. We used a simulation model based on data from Italian LPAI epidemics in turkeys to evaluate different surveillance strategies in terms of their performance as early warning systems. The strategies differed in terms of sample size, sampling frequency, diagnostic tests, and whether or not active surveillance (i.e., routine laboratory testing of farms) was performed, and were also tested under different epidemiological scenarios. We compared surveillance strategies by simulating within-farm outbreaks. The output measures were the proportion of infected farms that are detected and the farm reproduction number (R_h). The first one provides an indication of the sensitivity of the surveillance system to detect within-farm infections, whereas R_h reflects the effectiveness of outbreak detection (i.e., if detection occurs soon enough to bring an epidemic under control). Increasing the sampling frequency was the most effective means of improving the timeliness of detection (i.e., it occurs earlier), whereas increasing the sample size increased the likelihood of detection. Surveillance was only effective in preventing an epidemic if actions were taken within two days of sampling. The strategies were not affected by the quality of the diagnostic test, although performing both serological and virological assays increased the sensitivity of active surveillance. Early detection of LPAI outbreaks in turkeys can be achieved by increasing the sampling frequency for active surveillance, though very frequent sampling may not be sustainable in the long term. We suggest that, when no LPAI virus is circulating yet and there is a low risk of virus introduction, a less frequent sampling approach might be admitted, provided that the surveillance is intensified as soon as the first outbreak is detected.

Low pathogenicity avian influenza (LPAI) viruses of H5 and H7 subtypes have the potential to mutate into highly pathogenic strains (HPAI), which can threaten human health and cause huge economic losses. The current knowledge on the mechanisms of mutation from LPAI to HPAI is insufficient for predicting which H5 or H7 strains will mutate into an HPAI strain, and since the molecular changes necessary for the change in virulence seemingly occur at random, the probability of mutation depends on the number of virus replicates, which is associated with the number of birds that acquire infection. We estimated the transmission dynamics of LPAI viruses in turkeys using serosurveillance data from past epidemics in Italy. We fitted the proportions of birds infected in 36 flocks into a hierarchical model to estimate the basic reproduction number (R₀) and possible variations in R₀ among flocks caused by differences among farms. We also estimated the distributions of the latent and infectious periods, using experimental infection data with outbreak strains. These were then combined with the R₀ to simulate LPAI outbreaks and characterise the resulting dynamics. The estimated mean within-flock R₀ in the population of infected flocks was 5.5, indicating that an infectious bird would infect an average of more than five susceptible birds. The results also indicate that the presence of seropositive birds does not necessarily mean that the virus has already been cleared and the flock is no longer infective, so that seropositive flocks may still constitute a risk of infection for other flocks. In light of these results, the enforcement of appropriate restrictions, the culling of seropositive flocks, or pre-emptive slaughtering may be useful. The model and parameter estimates presented in this paper provide the first complete picture of LPAI dynamics in turkey flocks and could be used for designing a suitable surveillance program.

SUMMARY: Animal health surveillance enables the detection and control of animal diseases including zoonoses. Under the EU-FP7 project RISKSUR, a survey was conducted in 11 EU Member States and Switzerland to describe active surveillance components in 2011 managed by the public or private sector and identify gaps and opportunities. Information was collected about hazard, target population, geographical focus, legal obligation, management, surveillance design, risk-based sampling, and multi-hazard surveillance. Two countries were excluded due to incompleteness of data. Most of the 664 components targeted cattle (26·7%), pigs (17·5%) or poultry (16·0%). The most common surveillance objectives were demonstrating freedom from disease (43·8%) and case detection (26·8%). Over half of components applied risk-based sampling (57·1%), but mainly focused on a single population stratum (targeted risk-based) rather than differentiating between risk levels of different strata (stratified risk-based). About a third of components were multi-hazard (37·3%). Both risk-based sampling and multi-hazard surveillance were used more frequently in privately funded components. The study identified several gaps (e.g. lack of systematic documentation, inconsistent application of terminology) and opportunities (e.g. stratified risk-based sampling). The greater flexibility provided by the new EU Animal Health Law means that systematic evaluation of surveillance alternatives will be required to optimize cost-effectiveness.

The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production.