Challenges involved in the collection of appropriate data for the completion of disease outbreak risk assessments.

Risk assessment is an essential tool used in the control of disease outbreaks. Without it, key risk pathways might not be identified, resulting in potential spread of disease. The devastating effects of disease spread can ripple through society, affecting the economy and trade and having considerable impact on animal health and potentially human health. The World Organisation for Animal Health (WOAH, founded as OIE) has highlighted that risk analysis, which includes risk assessment, is not consistently used across all Members, with some low-income countries making policy decisions without prior risk assessment. The failure of some Members to rely on risk assessment could be caused by a lack of staff and risk assessment-related training, poor funding in the animal health sector, and lack of understanding regarding the use and application of risk analysis. However, to complete effective risk assessment, high-quality data must be collected, and other factors such as geographical conditions, use (or not) of technology, and varying production systems all influence the ability to collect these data. Demographic and population-level data can be collected during peacetime in the form of surveillance schemes and national reports. Having these data before an outbreak occurs better equips a country for controlling or preventing disease outbreaks. In order for all WOAH Members to meet risk analysis requirements, an international effort must be made for cross-working and the development of collaborative schemes. Technology can play an important role in the development of risk analysis, and low-income countries must not be left behind in the efforts to protect animal and human populations from disease.

L'évaluation du risque est un outil essentiel pour le contrôle des foyers épidémiques. Sans cet outil, certaines voies d'introduction à haut risque pourraient ne pas être identifiées, donnant lieu à une possible propagation des maladies. Une telle propagation a des effets dévastateurs qui peuvent gagner toute la société et affecter l'économie et le commerce, en plus d'avoir un impact considérable sur la santé des animaux, voire des personnes. L'Organisation mondiale de la santé animale (OMSA, fondée en tant qu'OIE) a souligné l'absence d'homogénéité parmi ses Membres dans l'utilisation de l'analyse du risque (dont l'évaluation du risque est l'une des composantes), certains pays à faibles revenus prenant des décisions sur les politiques à mener sans recourir à une évaluation du risque préalable. L'incapacité de certains Membres à s'appuyer sur l'évaluation du risque est probablement due au manque de personnel, à l'inexistence de formations dans le domaine de l'évaluation du risque, au financement médiocre du secteur de la santé animale et au fait que les principes de l'utilisation et de l'application de l'analyse du risque sont mal compris. Pour être efficace, l'évaluation du risque doit reposer sur des données de grande qualité ; or, l'aptitude à collecter de telles données est tributaire de facteurs comme la situation géographique, le recours (ou non) aux technologies, et la diversité des systèmes de production concernés. Les données démographiques ainsi que celles à l'échelle d'une population peuvent être recueillies en temps de paix dans le cadre de programmes de surveillance et sous forme de rapports nationaux. Un pays qui dispose de ces données avant la survenue d'un foyer épidémique est mieux équipé pour lutter contre les foyers ou pour les prévenir. Pour que tous les Membres de l'OMSA soient en mesure de satisfaire aux exigences de la conduite de l'analyse du risque, des initiatives devront être déployées à l'échelle internationale afin de mettre en place une coopération transversale et des programmes collaboratifs. La technologie peut jouer un rôle important dans le développement de l'analyse du risque et il convient de ne pas laisser les pays à faibles revenus en marge des efforts visant à protéger la santé des populations animales et humaines.

La determinación del riesgo es una herramienta esencial para el control de brotes infecciosos. Sin ella, podría ocurrir que se obviasen muy importantes vías de riesgo, lo que a su vez podría causar la diseminación de enfermedades. Los devastadores efectos de la propagación de una enfermedad pueden transmitirse en cadena al conjunto de la sociedad, afectando a la economía y el comercio y repercutiendo sensiblemente en la sanidad animal y quizá también en la salud humana. La Organización Mundial de Sanidad Animal (OMSA, fundada como OIE) ha insistido en que no todos los Miembros utilizan de forma sistemática el análisis del riesgo, proceso que engloba el de determinación del riesgo, y que algunos países de bajo nivel de renta adoptan decisiones sobre las medidas que se han de tomar sin pasar por un proceso previo de determinación del riesgo. Este déficit podría explicarse por factores como la falta de personal y de formaciones en determinación del riesgo, una insuficiente financiación del sector de la sanidad animal o el desconocimiento de los usos y aplicaciones del análisis del riesgo. Aun así, para realizar una eficaz determinación del riesgo es preciso reunir datos de gran calidad y, en este sentido, hay otros factores, como las condiciones geográficas, la utilización (o no) de tecnología o la heterogeneidad de los sistemas productivos, que siempre influyen en la capacidad para obtener tales datos. Es posible reunir datos demográficos y poblacionales "en tiempos de paz" por medio de sistemas de vigilancia y de informes nacionales. Si dispone de antemano de tales datos, un país estará mejor preparado para combatir o prevenir brotes infecciosos en el momento en que estos se produzcan. Para que todos los Miembros de la OMSA puedan efectuar los requeridos análisis del riesgo, se precisa un esfuerzo a escala internacional que se plasme en una labor de intercambio y en la creación de mecanismos de colaboración. La tecnología puede cumplir una importante función en el desarrollo del análisis del riesgo y, en el camino para proteger de la enfermedad a las poblaciones animales y humanas, de ningún modo cabe dejar atrás a los países de renta baja.

Determining pig holding type from British movement data using analytical and machine learning approaches.

Determining the size, location and structure of a livestock population is an essential aspect of surveillance and research as it provides understanding of the representativeness and coverage of any project or scheme. It is an important input for a variety of epidemiological analyses, for example, allowing generation of more accurate sample size calculations for estimating prevalence or freedom from disease, cost-benefit analyses for control measures to reduce or eradicate livestock disease, or development of between-herd network models to estimate the impact of movement of animals between farms on the spread of livestock diseases. The work described here provides information on how British pig movement data was compared against other datasets related to the British pig population to define its appropriateness for defining pig holding demographics. The data were then used to identify the location of pig holdings and the estimated herd size (split into five categories). Two methods are described that were used to classify the holding type of the identified pig holdings. The first method was an epidemiological method that used expert opinion to determine a set of rules based on movement characteristics to classify each holding. The second method was a machine learning approach that used k means cluster analysis to automatically estimate the holding type based on a set of proxy indicators. Each method had a good accuracy rate, when compared to matched holdings present in data provided by the Annual June Agricultural Survey, but all misclassified some holdings. While both of the methods on their own provided a reasonable estimate, it was concluded that a consensus model, considering the results of both models and the Survey, provided the most accurate result. However, the machine learning approach was beneficial, as although some technical expertise was needed to set up the model, it was considerably faster to implement than the other method, as well as being quicker and easier to adapt and re-run with updated information.

A cost-benefit assessment of Salmonella-control strategies in pigs reared in the United Kingdom.

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).

Longitudinal study on the occurrence in pigs of colistin-resistant Escherichia coli carrying mcr-1 following the cessation of use of colistin.

AIMS: In 2015, colistin-resistant Escherichia coli and Salmonella with the mcr-1 gene were isolated from a pig farm in Great Britain. Pigs were subsequently monitored over a ~20-month period for the occurrence of mcr-1-mediated colistin resistance and the risk of mcr-1 E. coli entering the food chain was assessed. METHODS AND RESULTS: Pig faeces and slurry were cultured for colistin-resistant E. coli and Salmonella, tested for the mcr-1 gene by PCR and selected isolates were further analysed. Seventy-eight per cent of faecal samples (n = 275) from pigs yielded mcr-1 E. coli after selective culture, but in positive samples only 0·2-1·3% of the total E. coli carried mcr-1. Twenty months after the initial sampling, faecal samples (n = 59) were negative for E. coli carrying mcr-1. CONCLUSIONS: The risk to public health from porcine E. coli carrying mcr-1 was assessed as very low. Twenty months after cessation of colistin use, E. coli carrying mcr-1 was not detected in pig faeces on a farm where it was previously present. SIGNIFICANCE AND IMPACT OF THE STUDY: The results suggest that cessation of colistin use may help over time to reduce or possibly eliminate mcr-1 E. coli on pig farms where it occurs.

Using multi-criteria risk ranking methodology to select case studies for a generic risk assessment framework for exotic disease incursion and spread through Europe.

We present a novel approach of using the multi-criteria pathogen prioritisation methodology as a basis for selecting the most appropriate case studies for a generic risk assessment framework. The approach uses selective criteria to rank exotic animal health pathogens according to the likelihood of introduction and the impact of an outbreak if it occurred in the European Union (EU). Pathogens were evaluated based on their impact on production at the EU level and international trade. A subsequent analysis included criteria of relevance to quantitative risk assessment case study selection, such as the availability of data for parameterisation, the need for further research and the desire for the case studies to cover different routes of transmission. The framework demonstrated is flexible with the ability to adjust both the criteria and their weightings to the user's requirements. A web based tool has been developed using the RStudio shiny apps software, to facilitate this.

Is there a decline in bovine spongiform encephalopathy cases born after reinforced feed bans? A modelling study in EU member states.

Occasional cases of classical bovine spongiform encephalopathy (BSE) still continue to occur within the European Union (EU) for animals born after reinforced feed bans (BARBs), which should in theory have eliminated all risk of infection. The study aimed to determine (i) whether a common rate of decline of BSE infection was evident across EU member states, i.e. to determine whether control measures have been equally effective in all member states, (ii) whether there was any evidence of spontaneous occurrence of BSE in the data and (iii) the expected date for the last BSE case in UK. It was found that there was no significant difference in the rate of decline of BSE prevalence between member states, with a common rate of decline of 33·9% per annum (95% CI 30·9-37%) in successive annual birth cohorts. Trend analysis indicated an ultimate decline to 0 prevalence, suggesting that spontaneous occurrence does not explain the majority of cases. Projecting forward the trends from the back-calculation model indicated that there was approximately a 50% probability of further cases in the UK, and should the current rate of decline continue, there remains the possibility of further occasional cases up until 2026.

An assessment of evidence data gaps in the investigation of possible transmission routes of extended spectrum ß-lactamase producing Escherichia coli from livestock to humans in the UK.

Antimicrobial resistance (AMR) threatens the effective prevention and treatment of bacterial diseases in both humans and animals. Globally, there has been much research done regarding resistant bacteria in the livestock industry, but few published resources collate this information. This report discusses a risk assessment (RA) framework and subsequent analysis of data availability for AMR in bacteria from 4 livestock sectors: dairy cattle, beef cattle, pigs and poultry, with particular reference to ESBL-producing Escherichia coli (ESBL E. coli) prevalence in the dairy cattle sector within the United Kingdom. The aim of this assessment was to identify where quality data exist, for the purpose of parameterising a quantitative RA, and where it would be useful to direct future research to provide quality data to improve the current knowledge base. Such research is necessary to support risk modelling and forecasting capability regarding the relative contributions of factors that maintain the emergence and spread of AMR in bacteria. The review suggested that there are data gaps regarding ESBL E. coli occurrence in the following: beef cattle, bulk tank milk and dairy products, animal-by-products, the farm environment (including after flooding) as well as the effect of animal stress on shedding levels. Filling these data gaps prior to undertaking a full quantitative RA would make the assessment more robust and give greater confidence in the final outcome and consequently inform the targeting and prioritising of interventions to minimise spread of AMR in bacteria in farm animals.

The challenge of using experimental infectivity data in risk assessment for Ebola virus: why ecology may be important.

Analysis of published data shows that experimental passaging of Zaire ebolavirus (EBOV) in guinea pigs changes the risk of infection per plaque-forming unit (PFU), increasing infectivity to some species while decreasing infectivity to others. Thus, a PFU of monkey-adapted EBOV is 10(7) -fold more lethal to mice than a PFU adapted to guinea pigs. The first conclusion is that the infectivity of EBOV to humans may depend on the identity of the donor species itself and, on the basis of limited epidemiological data, the question is raised as to whether bat-adapted EBOV is less infectious to humans than nonhuman primate (NHP)-adapted EBOV. Wildlife species such as bats, duikers and NHPs are naturally infected by EBOV through different species giving rise to EBOV with different wildlife species-passage histories (heritages). Based on the ecology of these wildlife species, three broad 'types' of EBOV-infected bushmeat are postulated reflecting differences in the number of passages within a given species, and hence the degree of adaptation of the EBOV present. The second conclusion is that the prior species-transmission chain may affect the infectivity to humans per PFU for EBOV from individuals of the same species. This is supported by the finding that the related Marburg marburgvirus requires ten passages in mice to fully adapt. It is even possible that the evolutionary trajectory of EBOV could vary in individuals of the same species giving rise to variants which are more or less virulent to humans and that the probability of a given trajectory is related to the heritage. Overall the ecology of the donor species (e.g. dog or bushmeat species) at the level of the individual animal itself may determine the risk of infection per PFU to humans reflecting the heritage of the virus and may contribute to the sporadic nature of EBOV outbreaks.

A Transport and Lairage Model for Salmonella Transmission Between Pigs Applicable to EU Member States.

A model for the transmission of Salmonella between finisher pigs during transport to the abattoir and subsequent lairage has been developed, including novel factors such as environmental contamination and the effect of stress, and is designed to be adaptable for any EU Member State (MS). The model forms part of a generic farm-to-consumption model for Salmonella in pigs, designed to model potentially important risk factors and assess the effectiveness of interventions. In this article, we discuss the parameterization of the model for two case study MSs. For both MSs, the model predicted an increase in the average MS-level prevalence of Salmonella-positive pigs during both transport and lairage, accounting for a large amount of the variation between reported on-farm prevalence and reported lymph-node prevalence at the slaughterhouse. Sensitivity analysis suggested that stress is the most important factor during transport, while a number of factors, including environmental contamination and the dose-response parameters, are important during lairage. There was wide variation in the model-predicted change in prevalence in individual batches; while the majority of batches (80-90%) had no increase, in some batches the increase in prevalence was over 70% and in some cases infection was introduced into previously uninfected batches of pigs. Thus, the model suggests that while the transport and lairage stages of the farm-to-consumption exposure pathway are unlikely to be responsible for a large increase in average prevalence at the MS level, they can have a large effect on prevalence at an individual-batch level.

Development of risk-based trading farm scoring system to assist with the control of bovine tuberculosis in cattle in England and Wales.

Identifying and ranking cattle herds with a higher risk of being or becoming infected on known risk factors can help target farm biosecurity, surveillance schemes and reduce spread through animal trading. This paper describes a quantitative approach to develop risk scores, based on the probability of infection in a herd with bovine tuberculosis (bTB), to be used in a risk-based trading (RBT) scheme in England and Wales. To produce a practical scoring system the risk factors included need to be simple and quick to understand, sufficiently informative and derived from centralised national databases to enable verification and assess compliance. A logistic regression identified herd history of bTB, local bTB prevalence, herd size and movements of animals onto farms in batches from high risk areas as being significantly associated with the probability of bTB infection on farm. Risk factors were assigned points using the estimated odds ratios to weight them. The farm risk score was defined as the sum of these individual points yielding a range from 1 to 5 and was calculated for each cattle farm that was trading animals in England and Wales at the start of a year. Within 12 months, of those farms tested, 30.3% of score 5 farms had a breakdown (sensitivity). Of farms scoring 1-4 only 5.4% incurred a breakdown (1-specificity). The use of this risk scoring system within RBT has the potential to reduce infected cattle movements; however, there are cost implications in ensuring that the information underpinning any system is accurate and up to date.