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The Zoonoses Action Plan (ZAP) Salmonella Programme was established by the British Pig Executive to monitor Salmonella prevalence in quality-assured British pigs at slaughter by testing a sample of pigs with a meat juice enzyme-linked immunosorbent assay for antibodies against group B and C1 Salmonella. Farms were assigned a ZAP level (1 to 3) depending on the monitored prevalence, and ZAP 2 or 3 farms were required to act to reduce the prevalence. The ultimate goal was to reduce the risk of human salmonellosis attributable to British pork. A mathematical model has been developed to describe the ZAP sampling protocol. Results show that the probability of assigning a farm the correct ZAP level was high, except for farms that had a seroprevalence close to the cutoff points between different ZAP levels. Sensitivity analyses identified that the probability of assigning a farm to the correct ZAP level was dependent on the sensitivity and specificity of the test, the number of batches taken to slaughter each quarter, and the number of samples taken per batch. The variability of the predicted seroprevalence was reduced . as the number of batches or samples increased and, away from the cutoff points, the probability of being assigned the correct ZAP level increased as the number of batches or samples increased. In summary, the model described here provided invaluable insight into the ZAP sampling protocol. Further work is required to understand the impact of the program for Salmonella infection in British pig farms and therefore on human health. [ABSTRACT FROM AUTHOR]

Those who work in the area of surveillance and prevention of emerging infectious diseases (EIDs) face a challenge in accurately predicting where infection will occur and who (or what) it will affect. Establishing surveillance and control programmes for EIDs requires substantial and long-term commitment of resources that are limited in nature. This contrasts with the unquantifiable number of possible zoonotic and non-zoonotic infectious diseases that may emerge, even when the focus is restricted to diseases involving livestock. Such diseases may emerge from many combinations of, and changes in, host species, production systems, environments/habitats and pathogen types. Given these multiple elements, risk prioritisation frameworks should be used more widely to support decision-making and resource allocation for surveillance. In this paper, the authors use recent examples of EID events in livestock to review surveillance approaches for the early detection of EIDs, and highlight the need for surveillance programmes to be informed and prioritised by regularly updated risk assessment frameworks. They conclude by discussing some unmet needs in risk assessment practices for EIDs, and the need for improved coordination in global infectious disease surveillance.

Drivers are factors that have the potential to directly or indirectly influence the likelihood of infectious diseases emerging or re-emerging. It is likely that an emerging infectious disease (EID) rarely occurs as the result of only one driver; rather, a network of sub-drivers (factors that can influence a driver) are likely to provide conditions that allow a pathogen to (re-)emerge and become established. Data on sub-drivers have therefore been used by modellers to identify hotspots where EIDs may next occur, or to estimate which sub-drivers have the greatest influence on the likelihood of their occurrence. To minimise error and bias when modelling how sub-drivers interact, and thus aid in predicting the likelihood of infectious disease emergence, researchers need good-quality data to describe these sub-drivers. This study assesses the quality of the available data on sub-drivers of West Nile virus against various criteria as a case study. The data were found to be of varying quality with regard to fulfilling the criteria. The characteristic with the lowest score was completeness, i.e. where sufficient data are available to fulfil all the requirements for the model. This is an important characteristic as an incomplete data set could lead to erroneous conclusions being drawn from modelling studies. Thus, the availability of good-quality data is essential to reduce uncertainty when estimating the likelihood of where EID outbreaks may occur and identifying the points on the risk pathway where preventive measures may be taken.

Machine learning (ML) is an approach to artificial intelligence characterised by the use of algorithms that improve their own performance at a given task (e.g. classification or prediction) based on data and without being explicitly and fully instructed on how to achieve this. Surveillance systems for animal and zoonotic diseases depend upon effective completion of a broad range of tasks, some of them amenable to ML algorithms. As in other fields, the use of ML in animal and veterinary public health surveillance has greatly expanded in recent years. Machine learning algorithms are being used to accomplish tasks that have become attainable only with the advent of large data sets, new methods for their analysis and increased computing capacity. Examples include the identification of an underlying structure in large volumes of data from an ongoing stream of abattoir condemnation records, the use of deep learning to identify lesions in digital images obtained during slaughtering, and the mining of free text in electronic health records from veterinary practices for the purpose of sentinel surveillance. However, ML is also being applied to tasks that previously relied on traditional statistical data analysis. Statistical models have been used extensively to infer relationships between predictors and disease to inform risk-based surveillance, and increasingly, ML algorithms are being used for prediction and forecasting of animal diseases in support of more targeted and efficient surveillance. While ML and inferential statistics can accomplish similar tasks, they have different strengths, making one or the other more or less appropriate in a given context.

Pork and pork products are a major source of human salmonellosis in the United Kingdom (UK). Despite a number of surveillance programmes, the prevalence of Salmonella in the UK slaughter pig population remains over 20%. Here, we present the results of a Cost-Benefit Analysis comparing five on-farm control strategies (where the cost is the cost of implementation and the benefits are the financial savings for both the human health and pig industries). The interventions considered were: wet feed, organic acids in feed, vaccination, enhanced cleaning and disinfection and movement of outdoor breeding units. The data originate from published papers and recent UK studies. The effectiveness was assessed by adapting a previous risk assessment, originally developed for the European Food Safety Authority. Using this method, none of the intervention strategies produced a net cost-benefit. Our results suggest that the cost of implementation outweighed the savings for all interventions, even if the effectiveness could be improved. Therefore, to achieve a net cost-benefit it is essential to reduce the cost of interventions. Analyses concluded that large cost reductions (up to 96%) would be required. Use of organic acids required the smallest reduction in cost (22.7%) to achieve a net cost benefit. Uncertainty analysis suggested that a small net gain might be possible, for some of the intervention measures. But this would imply that the model greatly underestimated some key parameters, which was considered unlikely. Areas of key uncertainty were identified as the under-reporting factor (i.e. the proportion of community cases of Salmonella) and the source attribution factor (i.e. the proportion of human Salmonella cases attributable to pork products)., (Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.)

The number and proportion of CTX-M positive Escherichia coli organisms were determined in feces from cattle, chickens, and pigs in the United Kingdom to provide a better understanding of the risk of the dissemination of extended-spectrum β-lactamase (ESBL) bacteria to humans from food animal sources. Samples of bovine (n = 35) and swine (n = 20) feces were collected from farms, and chicken cecal contents (n = 32) were collected from abattoirs. There was wide variation in the number of CTX-M-positive E. coli organisms detected; the median (range) CFU/g were 100 (100 × 10(6) to 1 × 10(6)), 5,350 (100 × 10(6) to 3.1 × 10(6)), and 2,800 (100 × 10(5) to 4.7 × 10(5)) for cattle, chickens, and pigs, respectively. The percentages of E. coli isolates that were CTX-M positive also varied widely; median (range) values were 0.013% (0.001 to 1%) for cattle, 0.0197% (0.00001 to 28.18%) for chickens, and 0.121% (0.0002 to 5.88%) for pigs. The proportion of animals designated high-density shedders (≥1 × 10(4) CFU/g) of CTX-M E. coli was 3/35, 15/32, and 8/20 for cattle, chickens, and pigs, respectively. We postulate that high levels of CTX-M E. coli in feces facilitate the dissemination of bla(CTX-M) genes during the rearing of animals for food, and that the absolute numbers of CTX-M bacteria should be given greater consideration in epidemiological studies when assessing the risks of food-borne transmission.

The use of antibiotics for animal growth promotion has been controversial because of the potential transfer of antibiotic resistance from animals to humans. Such transfer could have severe public health implications in that treatment failures could result. We have followed a risk assessment approach to evaluate policy options for the streptogramin-class of antibiotics: virginiamycin, an animal growth promoter, and quinupristin/dalfopristin, a antibiotic used in humans. Under the assumption that resistance transfer is possible, models project a wide range of outcomes depending mainly on the basic reproductive number (R(0)) that determines the potential for person-to-person transmission. Counter-intuitively, the benefits of a ban on virginiamycin were highest for intermediate values of R(0), and lower for extremely high or low values of R(0).