This paper reviews recent progress in the development of syndromic surveillance systems for veterinary medicine. Peer-reviewed and grey literature were searched in order to identify surveillance systems that explicitly address outbreak detection based on systematic monitoring of animal population data, in any phase of implementation. The review found that developments in veterinary syndromic surveillance are focused not only on animal health, but also on the use of animals as sentinels for public health, representing a further step towards One Medicine. The main sources of information are clinical data from practitioners and laboratory data, but a number of other sources are being explored. Due to limitations inherent in the way data on animal health is collected, the development of veterinary syndromic surveillance initially focused on animal health data collection strategies, analyzing historical data for their potential to support systematic monitoring, or solving problems of data classification and integration. Systems based on passive notification or data transfers are now dealing with sustainability issues. Given the ongoing barriers in availability of data, diagnostic laboratories appear to provide the most readily available data sources for syndromic surveillance in animal health. As the bottlenecks around data source availability are overcome, the next challenge is consolidating data standards for data classification, promoting the integration of different animal health surveillance systems, and also the integration to public health surveillance. Moreover, the outputs of systems for systematic monitoring of animal health data must be directly connected to real-time decision support systems which are increasingly being used for disease management and control.
This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain.
Currently, the aetiology of runting and stunting syndrome (RSS) in chickens is unknown. The impact of RSS on weight gain and microscopic lesions in immunological organs and the duodenum, was investigated in 1-day-old commercial broilers at 12 days following exposure to RSS-contaminated litter. Furthermore, the presence of the viral nucleic acids of three astroviruses and one parvovirus was analysed by in situ hybridization from days 1 through 5 post exposure. A 70% decrease in weight was observed in the RSS-exposed group at the end of the experiments when compared with the unexposed controls. Lesions in the bursa of Fabricius and thymus were present in both groups but were significantly higher at the end of the study in the RSS-exposed group. In contrast, no significant difference in Harderian gland lesions was observed between the groups. Histological lesions in the duodenum were already present 24 h after exposure in the RSS-exposed group only, peaked at day 4 and declined until the end of the study. Results of the in situ hybridization studies clearly indicate replication of three astroviruses (chicken astrovirus, avian nephritis virus [ANV]-1, ANV-2) in the duodenum but not in other organs evaluated. Chicken astrovirus nucleic acids were detected on days 1 and 2 post exposure, while ANV-1 and ANV-2 nucleic acids were observed on several days during the period investigated. Surprisingly, no viral nucleic acid specific for the chicken parvovirus was observed. The results indicate that astroviruses probably play an important role during RSS due to the concurrence of viral RNA detection and lesions in the duodenum.
While measures to control carcass contamination with Salmonella at the processing plant have been implemented with some success, on-farm interventions that reduce Salmonella prevalence in meat birds entering the processing plant have not translated well on a commercial scale. We determined the impact of Salmonella vaccination on commercial poultry operations by monitoring four vaccinated and four nonvaccinated breeder (parental) chicken flocks and comparing Salmonella prevalences in these flocks and their broiler, meat bird progeny. For one poultry company, their young breeders were vaccinated by using a live-attenuated Salmonella enterica serovar Typhimurium vaccine (Megan VAC-1) followed by a killed Salmonella bacterin consisting of S. enterica serovar Berta and S. enterica serovar Kentucky. The other participating poultry company did not vaccinate their breeders or broilers. The analysis revealed that vaccinated hens had a lower prevalence of Salmonella in the ceca (38.3% versus 64.2%; P < 0.001) and the reproductive tracts (14.22% versus 51.7%; P < 0.001). We also observed a lower Salmonella prevalence in broiler chicks (18.1% versus 33.5%; P < 0.001), acquired from vaccinated breeders, when placed at the broiler farms contracted with the poultry company. Broiler chicken farms populated with chicks from vaccinated breeders also tended to have fewer environmental samples containing Salmonella (14.4% versus 30.1%; P < 0.001). There was a lower Salmonella prevalence in broilers entering the processing plants (23.4% versus 33.5%; P < 0.001) for the poultry company that utilized this Salmonella vaccination program for its breeders. Investigation of other company-associated factors did not indicate that the difference between companies could be attributed to measures other than the vaccination program.
Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.
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