Search

The description of the pattern of livestock movements between herds provides essential information for both improving risk-based surveillance and to understand the likely spread of infectious diseases. This study provides a description of the temporal pattern of pig movements recorded in Italy on a 4-year period (2013–2016). Data, provided by the National Livestock registry, were described by social network analysis and the application of a walk-trap algorithm for community detection. Our results show a highly populated community located in Northern Italy, which is the focal point of the Italian industrial pig production and as a general pattern an overall decline of medium and backyard farms and an increase in the number of large farms, in agreement with the trend observed by other EU pig-producing countries. A seasonal pattern of all the parameters evaluated, including the number of active nodes in both the intensive and smaller production systems, emerged: that is characterized by a higher number of movements in spring and autumn, linked with the breeding and production cycle as pigs moved from the growing to the finishing phase and with periods of increased slaughtering at Christmas and Easter. The same pattern was found when restricting the analysis to imported pig batches. Outbreaks occurring during these periods would have a greater impact on the spread of infectious diseases; therefore, targeted surveillance may be appropriate. Finally, potential super-spreader nodes have been identified and represent 0.47% of the total number of pig holdings (n = 477). Those nodes are present during the whole study period with a similar ranking in their potential of being super-spreaders. Most of them were in Northern Italy, but super-spreaders with high mean out-degree centrality were also located in other Regions. Seasonality, communities and super-spreaders should be considered when planning surveillance activity and when applying disease control strategies.

An ongoing, continually spreading, outbreak of African swine fever (ASF), following its identification in Georgia in 2007, has resulted in 17 European and 12 Asian countries reporting cases by April 2020, with cases occurring in both wild boar and domestic pigs. Curtailing further spread of ASF requires understanding of the transmission pathways of the disease. ASF is self-sustaining in the wild boar population, and they have been implicated as one of the main drivers of transmission within Europe. We developed a spatially explicit model to estimate the risk of infection with ASF in wild boar and pigs due to natural movement of wild boar that is applicable across the whole of Europe. We demonstrate the model by using it to predict the probability that early cases of ASF in Poland were caused by wild boar dispersion. The risk of infection in 2015 is computed due to wild boar cases in Poland in 2014, compared against reported cases in 2015, and then the procedure is repeated for 2015-2016. We find that long- and medium-distance spread of ASF (i.e. >30 km) is unlikely to have occurred due to wild boar dispersal, due in part to the generally short distances wild boar will travel (

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.

Highlights • Risk assessment for entry of zoonotic bat-borne viruses into the European Union. • Comparison of relative risk of incursion of five viruses by four routes. • An Ebola outbreak similar to 2013–2016 posed the highest risk of introduction. • Relative risk to countries varied due to extent of global trade and human travel. • Model provides a horizon scanning tool for use when available data is limited., This paper presents a quantitative assessment model for the risk of entry of zoonotic bat-borne viruses into the European Union (EU). The model considers four routes of introduction: human travel, legal trade of products, live animal imports and illegal import of bushmeat and was applied to five virus outbreak scenarios. Two scenarios were considered for Zaire ebolavirus (wEBOV, cEBOV) and other scenarios for Hendra virus, Marburg virus (MARV) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). The use of the same framework and generic data sources for all EU Member States (MS) allows for a relative comparison of the probability of virus introduction and of the importance of the routes of introduction among MSs. According to the model wEBOV posed the highest risk of an introduction event within the EU, followed by MARV and MERS-CoV. However, the main route of introduction differed, with wEBOV and MERS-CoV most likely through human travel and MARV through legal trade of foodstuffs. The relative risks to EU MSs as entry points also varied between outbreak scenarios, highlighting the heterogeneity in global trade and travel to the EU MSs. The model has the capability to allow for a continual updating of the risk estimate using new data as, and when, it becomes available. The model provides an horizon scanning tool for use when available data are limited and, therefore, the absolute risk estimates often have high uncertainty. Sensitivity analysis suggested virus prevalence in bats has a large influence on the results; a 90% reduction in prevalence reduced the risk of introduction considerably and resulted in the relative ranking of MARV falling below that for MERS-CoV, due to this parameter disproportionately affecting the risk of introduction from the trade route over human travel.

Timely and reliable data and information availability and sharing is essential for early warning, prevention and control of transboundary diseases. While there are a growing number of global datasets capable of providing information for use in early warning systems and risk assessment (RA) tools, there are currently time-consuming data cleansing and harmonisation activities which need to be carried out before they can be reliably used and combined. Thus, using global datasets as they stand can lead to errors in RA parameterisation and results due to inherent biases in the data, e.g. missing disease prevalence data treated as a zero may inadvertently penalise those countries which do report disease outbreaks as opposed to those countries which are affected by a pathogen but do not report outbreak data. It is therefore of great importance that data are clearly provided and easy to understand and that data providers strive for greater harmonisation of database standards. In this paper the datasets utilised in the SPARE ('Spatial risk assessment framework for assessing exotic disease incursion and spread through Europe') project are described and discussed in terms of key criteria: accessibility, availability, completeness, consistency and quality. It is evident that most databases exist as information portals and not exclusively for RA purposes. Another striking issue from this assessment is the need for enhanced data sharing specifically with regards to data on illegal seizures, arthropod vector/wildlife abundance, intra-country livestock movement and national animal disease surveillance. It is hoped that the outcomes of this work will promote discussion and exchange between data providers, including the development of standardised data exchange protocols. The transformation of datasets to a common format is a considerable challenge but recommendations could and should be made on the standardisation of datasets and reporting in order to achieve a unified approach across Europe.

SummaryAfrican swine fever (ASF) has been causing multiple outbreaks in Russia, Poland and the Baltic countries in recent years and is currently spreading westwards throughout Europe and eastwards into China, with cases occurring in wild boar and domestic pigs. Curtailing further spread of ASF requires full understanding of the transmission pathways of the disease. Wild boars have been implicated as a potential reservoir for the disease and one of the main modes of transmission within Europe. We developed a spatially explicit model to estimate the risk of infection with ASF in boar and pigs due to the natural movement of wild boar that is applicable across the whole of Europe. We demonstrate the model by using it to predict the probability that early cases of ASF in Poland were caused by wild boar dispersion. The risk of infection in 2015 is computed due to wild boar cases in Poland in 2014, compared against the reported cases in 2015 and then the procedure is repeated for 2015-2016. We find that long- and medium-distance spread of ASF (i.e. >30km) is very unlikely to have occurred due to boar dispersal, due in part to the generally short distances boar will travel (

European surveillance for Bovine Spongiform Encephalopathy (BSE), initiated in 2001, has shown a steady exponential decline in the number of infected cattle, demonstrating that control measures have been effective. In 2016 23 European countries out of 28 demonstrated negligible risk status for the disease. The international standard setting body, the World Organization for Animal Health (OIE), prescribes that for countries where there is a non-negligible BSE risk, surveillance should allow the detection of one case per 100,000 in the adult cattle population with 95% confidence (Type A surveillance). The Cattle TSE Monitoring Model (C-TSEMM) was developed to estimate the sensitivity of surveillance systems to detect BSE in cattle. The model includes a cohort-based back calculation model to estimate the number and age of infected animals, the subset of those that are detectable by the diagnostic test, and the stream by which infected animals exit the standing population, that is, healthy slaughter, emergency slaughter, fallen stock and clinical suspects. Data collected by the European Commission (EC) and European Food Safety Authority (EFSA), containing the details of over 91 million cattle tested within the European Member States (EU25) surveillance scheme from 2002 to 2011, was used to populate the model. When considering the EU25 surveillance scheme as a single epidemiological unit, the model estimated that the surveillance scheme in place exceeded the OIE required threshold for Type A surveillance, and that a revised monitoring system excluding all healthy slaughter animals would also exceed this threshold. Results indicated a variation in individual country sensitivity of surveillance with seven countries (Germany, Spain, France, Ireland, Italy, Poland and the UK) exceeding the required threshold individually. Key assumptions included the fitting of an exponential distribution for all countries to describe the decline in prevalence over time and, for those members with no detected cases of BSE, an estimated area prevalence was used as a proxy. These results were part of the evidence base that lifted the testing requirement for healthy slaughter animals in European countries in 2013. The model has been maintained and updated by EFSA each year with the addition of new member states, and updated surveillance data.

Vector borne diseases are a continuing global threat to both human and animal health. The ability of vectors such as mosquitos to cover large distances and cross country borders undetected provide an ever-present threat of pathogen spread. Many diseases can infect multiple vector species, such that even if the climate is not hospitable for an invasive species, indigenous species may be susceptible and capable of transmission such that one incursion event could lead to disease establishment in these species. Here we present a consensus modelling methodology to estimate the habitat suitability for presence of mosquito species in the UK deemed competent for Rift Valley fever virus (RVF) and demonstrate its application in an assessment of the relative risk of establishment of RVF virus in the UK livestock population. The consensus model utilises observed UK mosquito surveillance data, along with climatic and geographic prediction variables, to inform six independent species distribution models; the results of which are combined to produce a single prediction map. As a livestock host is needed to transmit RVF, we then combine the consensus model output with existing maps of sheep and cattle density to predict the areas of the UK where disease is most likely to establish in local mosquito populations. The model results suggest areas of high suitability for RVF competent mosquito species across the length and breadth of the UK. Notable areas of high suitability were the South West of England and coastal areas of Wales, the latter of which was subsequently predicted to be at higher risk for establishment of RVF due to higher livestock densities. This study demonstrates the applicability of outputs of species distribution models to help predict hot-spots for risk of disease establishment. While there is still uncertainty associated with the outputs we believe that the predictions are an improvement on just using the raw presence points from a database alone. The outputs can also be used as part of a multidisciplinary approach to inform risk based disease surveillance activities.

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.

Classical Swine Fever (CSF) is a porcine viral disease that has severe consequences on animal health, welfare, and production. In Switzerland six broad levels of on-farm intervention measures are considered depending on the assessment of the epidemiological status of the focal farm, of the neighbouring farms and of the country; Basic biosecurity (I0), Basic biosecurity with reinforced surveillance (I0b), Surveillance zone (I1), Protection Zone (I2), Quarantine (I3) and Culling (I4). In 2014 an exclusion component was added to allow testing for CSF without implementing quarantine or movement restrictions. The objective of this study was to assess the cost-effectiveness of this current system. A stochastic farm-based model was developed which simulates the spread of CSF through Swiss pig farms in space and time, taking account of the implementation of intervention measures based on the system of surveillance. Two measures of effectiveness of the current system were assessed 1) the number of infected farms per outbreak and 2) the probability of implementing an intervention strategy (I0-I4) that is not strong enough given the true epidemiological status. Costs of the outbreak included the cost of implementing interventions and the costs of culling all animals determined to be infected over the course of the outbreak The median number of infected farms per outbreak was 80. Among all the simulations, 1.4% (14/1000 simulations) did not lead to an outbreak. The median probabilities of errors, over all simulations, for the different possible intervention measures to be implemented at the time when a farm becomes infectious were highest for basic biosecurity level with reinforced surveillance (I0b), then surveillance zone measures (I1), basic biosecurity level (I0), and finally protection zone measures (I2) with the lowest probability. The median costs endorsed at federal level were 3.4 million CHF per outbreak. The cost-effectiveness of the system was evaluated as medium. The exclusion component was evaluated as good, balancing its low price of implementation, the level of biosecurity implemented at farms and the risk of CSF incursion in Switzerland. We suggest that changes in the surveillance strategy that would increase the probability of reinforced surveillance (I0b, I1, I2) to be implemented at the time a farm becomes infectious would increase the cost-effectiveness of the system. Such changes could include increasing awareness among stakeholders or developing new actions after the first outbreak, such as more intense investigations among connected farms.

Exotic animal diseases are transboundary hazards, characterized by their capability to cover global distances, affecting animal health and welfare with significant economic losses. Their prevention is complex and requires the dynamic management of potential entry points, transmission pathways, and preventative barriers. The well-timed detection of an undefined or unexpected (exotic or re-emerging) threat could minimize the consequences due to onward transmission. As a fit for purpose framework, OIE developed the import risk assessment i.e. a risk assessment model focusing on the entrance of an exotic disease into a geographical area with naive hosts. In this paper, we propose an improvement of the model by integrating it with Social Network Analysis (SNA) accounting for within-country animal movements. Our integrated model has been used as a combined tool to better estimate the spatial probability of the introduction of at least one affected animal in Italian provinces using Bluetongue (BT) as an example. Starting from international country-specific BT prevalence data, the model estimated the probability of introduction to Italy via two different routes of release i.e. the import of infected animals or the release of infected vectors either associated with imported livestock or through windborne dispersion from Africa. The conventional OIE model estimating the probability of BT entering Italy assuming the same release probability for every Italian province was paralleled by a model integrated with outputs from SNA to account for different release probability among provinces based on animal movements. The conventional model predicted a remarkable homogeneity in the risk among the provinces with some peaks only visible during the warmest months. The model incorporating the network analysis predicted the highest risk to be in the North Eastern region of Italy but also highlighted the likely occurrence in a couple of Southern provinces, an output mirroring past occurrence of BT in Italy. Moreover, the sensitivity analysis highlighted the main role for a couple of model parameters i.e. the probability for a vector to become infected and the vaccine coverage, thus suggesting that an extra effort in vaccine campaigns could be envisaged. The ability to measure animal movements by SNA can allow the identification of geographical risk hot spots and therefore the risk-based targeting of the surveillance system.

Modelling the range of prominent vectors of pathogens is a common technique used for planning surveys, building exposure assessment estimations or addressing the impact of future climate scenarios. In most cases the recorded distribution of a target species is used together several environmental variables to capture its potential distribution, ignoring the availability of adequate hosts for the arthropod. In this study we used satellite-recorded, Fourier transformed, environmental variables of ground temperature and vegetation (NDVI) and a large dataset of records of the tick Ixodes ricinus and their hosts. The aim is to evaluate the influence of the contact rates of the ticks with the different hosts and how they impact on the raw, environmental-based distribution of the tick. These effects may alter the estimations of the distribution of the tick under future climate scenarios. This study demonstrated that the relative contribution of the hosts supporting the feeding of the tick life stages dramatically altered the expected distribution of the tick. In the study case, results support that both Passeriformes (Aves) and Rodentia-Soricomorpha contribute in different ways to the colonization of the tick along a large latitudinal gradient. However, Artiodactyla are the main supporters of the tick and enhance its presence in the fringe of its distribution area. Thus, complete data on the distribution of each host contributing to the tick's life cycle and their relative importance for the tick, should be included in the assessment of exposure by arthropods. Other results of the study suggested that more information could be obtained exploring the association of the arthropod to the variables in the environmental niche, instead of the classic approach based on a crude evaluation on the geographical space (maps).

A farm-to-consumption quantitative microbiological risk assessment (QMRA) for Salmonella in pigs in the European Union has been developed for the European Food Safety Authority. The primary aim of the QMRA was to assess the impact of hypothetical reductions of slaughter-pig prevalence and the impact of control measures on the risk of human Salmonella infection. A key consideration during the QMRA development was the characterization of variability between E.U. Member States (MSs), and therefore a generic MS model was developed that accounts for differences in pig production, slaughterhouse practices, and consumption patterns. To demonstrate the parameterization of the model, four case study MSs were selected that illustrate the variability in production of pork meat and products across MSs. For the case study MSs the average probability of illness was estimated to be between 1 in 100,000 and 1 in 10 million servings given consumption of one of the three product types considered (pork cuts, minced meat, and fermented ready-to-eat sausages). Further analyses of the farm-to-consumption QMRA suggest that the vast majority of human risk derives from infected pigs with a high concentration of Salmonella in their feces (≥10(4) CFU/g). Therefore, it is concluded that interventions should be focused on either decreasing the level of Salmonella in the feces of infected pigs, the introduction of a control step at the abattoir to reduce the transfer of feces to the exterior of the pig, or a control step to reduce the level of Salmonella on the carcass post-evisceration.

In response to the European Food Safety Authority's wish to assess the reduction of human cases of salmonellosis by implementing control measures at different points in the farm-to-consumption chain for pork products, a quantitative microbiological risk assessment (QMRA) was developed. The model simulated the occurrence of Salmonella from the farm to consumption of pork cuts, minced meat, and fermented ready-to-eat sausage, respectively, and a dose-response model was used to estimate the probability of illness at consumption. The QMRA has a generic structure with a defined set of variables, whose values are changed according to the E.U. member state (MS) of interest. In this article we demonstrate the use of the QMRA in four MSs, representing different types of countries. The predicted probability of illness from the QMRA was between 1 in 100,000 and 1 in 10 million per serving across all three product types. Fermented ready-to-eat sausage imposed the highest probability of illness per serving in all countries, whereas the risks per serving of minced meat and pork chops were similar within each MS. For each of the products, the risk varied by a factor of 100 between the four MSs. The influence of lack of information for different variables was assessed by rerunning the model with alternative, more extreme, values. Out of the large number of uncertain variables, only a few of them have a strong influence on the probability of illness, in particular those describing the preparation at home and consumption.

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 burden of Salmonella entering pig slaughterhouses across the European Union is considered a primary food safety concern. To assist E.U. member states with the development of national control plans, we have developed a farm transmission model applicable to all member states. It is an individual-based stochastic susceptible-infected model that takes into account four different sources of infection of pigs (sows, feed, external contaminants such as rodents, and new stock) and various management practices linked to Salmonella transmission/protection (housing, flooring, feed, all-in-all-out production). A novel development within the model is the assessment of dynamic shedding rates. The results of the model, parameterized for two case study member states (one high and one low prevalence) suggest that breeding herd prevalence is a strong indicator of slaughter pig prevalence. Until a member state's' breeding herd prevalence is brought below 10%, the sow will be the dominant source of infection to pigs raised for meat production; below this level of breeding herd prevalence, feed becomes the dominant force of infection.

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).

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.

SUMMARYOccasional 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.

Since the beginning of the 21st century, consumption of wild game birds has been increasing, concurrent with a rise in consumer interest in free-range and ‘healthy-eating’ foodstuffs. Game birds can carry zoonotic pathogens, predominantly within the viscera. Whilst removal of the viscera is normal practice during the dressing of game birds, there is a specialised market in the UK for certain small game birds that are consumed uneviscerated. This qualitative risk assessment evaluates the risks to the consumer from the production and consumption of both uneviscerated and eviscerated small game birds for Salmonella spp., Escherichia coli (verotoxigenic), E. coli (antimicrobial resistant), Campylobacter spp., Toxoplasma gondii, Chlamydophila psittaci and Listeria monocytogenes. Whilst most pathogen/bird combinations were considered to have a very low risk, results suggest that Campylobacter spp. and T. gondii can pose an increased risk to consumers for some species of wild game birds. There was no greater risk associated with the consumption of uneviscerated game birds than for eviscerated birds. In some cases, the risk from eviscerated birds may even be slightly higher, as the risk of cross-contamination during evisceration can outweigh the reduction in pathogenic organisms due to removal of the viscera. Additionally, the current low frequency of consumption of uneviscerated game birds of most species reduced the overall risk estimate for these birds. If there is an increased frequency of consumption in the future, then this risk should be re-examined. Assuming a general level of compliance with regulations and basic hygiene practices, the results suggested that large outbreaks of infection among UK consumers are unlikely, but sporadic, infectious events could occur due to combinations of ‘rare-event, hygiene-related issues’ in the ‘field-to-fork’ chain and/or inadequate cooking of the bird.

Visualisation and presentation of complex, feature rich data is often neglected within the area of quantitative risk assessment when, in reality, a clear representation of the data may greatly support the understanding of complex results and increase acceptance of results among risk managers. Feature rich data containing many explanatory variables can be very challenging to visualise effectively in a single graphic as the message can become cluttered when using two, or more, variables. As part of the international SPARE project (SPARE project team, 2018), a generic quantitative risk assessment (QRA) combining multiple routes and routes of entry was developed (Simons et al., 2019). The risk assessment provides an overall risk score for entry and exposure of three different pathogens into individual European Union (EU) countries: Classical swine fever (CSF), Bluetongue virus (BTV) and classical rabies. This paper describes the development of an application with graphical user interface (GUI) that allowed users, in particular risk managers, to explore the data and calculations behind the QRA. The aim was to produce an application that clearly illustrates the data and communicates the information without biasing their opinion in any way. To reach as wide an audience as possible the application should be easily accessed and understood without requiring the user to have detailed technical computing knowledge to use it. Development of the application followed a set of design principles focused on: context, software, data inputs and interactivity. This produced an application designed for a particular audience, written in a suitable language that allowed the user to explore the data within the context of a clearly defined risk question. Accessibility options for users with visual challenges was also considered and included within the design phase.

Disease incursion and transmission modelling can play an important role in elucidating important pathways and dynamics of transboundary diseases. It is an important pre-requisite for preparedness and rapid response. A model framework has been developed which makes use of global datasets to predict the probability of entry of exotic animal pathogens to European Union (EU) member states (MSs) via some of the most likely routes of introduction: legal trade of livestock and meat products, illegal trade of red meat, wild animal dispersion, windborne vector dispersion and human introduction of pets. The model was designed to be applicable for a wide range of pathogens, many of which have limited data. We demonstrate its application through four case study pathogens: African swine fever, Classical swine fever, Bluetongue and classical rabies. The model results highlight the differences in probability between EU MSs; the absolute values for entry via a given route differed across MSs whilst different pathogens were predicted as having the highest probability of entry for the same route across MSs. Scenario analyses suggested that the probability of entry was heavily influenced by the pathogen prevalence in the country of origin and the extent to which EU MSs pose a risk to each other; the greatest risk was predominantly from countries within the EU. While we believe the input data are obtained from high quality sources, there are still big issues with regards uncertainty in some areas, in particular with regards to prevalence of pathogens in vector populations and consistency of reporting of pathogen prevalence in animals across all countries of the world. Thus, it is inevitable that there is a high degree of uncertainty associated with the absolute values. However, the main strength of the model is the broad range of analyses over pathogens, EU MSs and routes of entry. The model is also relatively easy to update with new data and a web based visualisation tool has been developed which allows users to interrogate the results of the model. As such, we believe that the model proposed here can be a useful quantitative complement to current qualitative early warning systems, helping to drive risk-based surveillance activities, by providing detailed quantitative comparisons to indicate which pathogens are most likely to enter the EU, by which route and into which areas within Europe.

Decision-makers and risk managers are often called upon to prioritise on and recommend suitable measures to prevent the risk of introduction and spread of pathogens. The main objective of this study was to assess the perceptions of experts in Italy, Switzerland and the United Kingdom concerning the importance, effectiveness, feasibility, costs and acceptability of selected biosecurity measures to prevent the introduction and limit the spread of rabies, blue tongue (BT) and classical swine fever (CSF). After identifying the most relevant measures by the project team, an expert knowledge elicitation was implemented through a questionnaire. After preliminary descriptive analyses, a number of statistical calculations were performed such as weighted medians, Spearman rank correlation tests, Wilcoxon comparison tests and ranking of measures. Three experts from each country completed the questionnaires, one expert for each disease. The mean answer rates for CSF, BT and rabies were 73%, 100% and 99% respectively. "Tracing system for live animal trade" was highlighted as very relevant in all diseases. The implementation of a "restriction zone after a suspicion or confirmation" was also rated as a relevant measure, especially for CSF. We identified generally a small correlation between costs and the other criteria. Among the rabies experts, measures related to "zoonotic risk" were rated highly, supporting the idea of a One Health approach. Disagreement among experts concerned 43 measures for the three pathogens: the debated measures were "control of the wildlife CSF status", "arthropod-vector control" and "rabies vaccination for domestic animals". Facing budget restriction, decision-makers need to prioritise their actions and make efficient prevention choices. With this study, we aimed to provide elements for reflection and to inform priority setting. The results can be applied through the implementation of similar surveys or directly from the knowledge already gathered in this study.

During the bovine spongiform encephalopathy (BSE) epidemic in July 2001 the European Commission established a surveillance scheme for the comprehensive sampling of all BSE clinical suspects, healthy slaughter (HS) animals30months, and all emergency slaughter and fallen stock animals tested when24months. With the exponential decline in classical BSE cases, this comprehensive surveillance system has been successively modified to become risk-based, targeting those exit streams and ages where cases from the original epidemic are most likely to be detected. Such reductions in testing are not without losses in the information subsequently collected, which could affect the sensitivity of the surveillance system to relatively small changes in the underlying prevalence of BSE across the European Union (EU). Here we report on a cohort-based approach to estimate the time taken for EU surveillance to observe a theoretical re-emergence of BSE in cattle. A number of surveillance schemes were compared. The baseline scheme considered detection being triggered by at least one case in the 'age window' 48-72 months in the fallen stock or emergency slaughter exit streams. Alternative schemes changed the start and end of this age window as well as considering testing for HS cattle. Under the baseline scheme, an estimated 15 years would lapse ([2.5th, 97.5th] percentiles=[10,24]) prior to detection, during which time 2867 infected animals ([2.5th, 97.5th]=[1722,6967]) would enter the slaughter population. These animals would be predominantly young animals (majority24months) showing no clinical signs. This baseline scheme reduced the time to detection by 2 years, compared to a scheme where only clinical suspects were tested assuming BSE symptoms are recognised to the same degree by veterinary surgeons. Additional testing of younger animals did not improve detection as young infected animals were unlikely to test positive, but testing of older animals reduced the time to detection. Testing of HS animals72months reduced the time to detection by one year compared to the baseline model, but would incur a high financial cost, e.g. testing HS animals72months of age for 14 years would entail approximately 50.4 million additional tests. A limitation of the results is that there is no guarantee that current detection methods, optimised for detection of classical BSE, would identify a novel prion disease in cattle and it is currently difficult to envisage plausible routes by which a re-emergence of classical BSE could occur in Europe.

SUMMARYThis is the first study comparing societal costs of acute illness with Salmonella Typhimurium (ST) and Salmonella Enteritidis (SE) in the UK. It included the cost and severity of the illness and explored the impact of each Salmonella serovar on the patients, their families, the NHS, and the wider economy. The study ascertained confirmed cases of ST and SE between July and November 2008. The mean costs per case were £1282 (ST) and £993 (SE). The indirect costs associated with the work-time lost by the case, parents, or carers were £409 (ST) and £228 (SE); this difference was statistically significant. The aggregate cost of ST and SE identified using laboratory test results for the UK as a whole was estimated as £6.5 million. Work-time lost and caring activities are cost categories that are not frequently investigated within the infectious intestinal disease literature, although they represent an important societal cost.

To address the risk posed to human health by the consumption of VTEC O157 within contaminated pork, lamb, and beef products within Great Britain, a quantitative risk assessment model has been developed. This model aims to simulate the prevalence and amount of VTEC O157 in different meat products at consumption within a single model framework by adapting previously developed models. The model is stochastic in nature, enabling both variability (natural variation between animals, carcasses, products) and uncertainty (lack of knowledge) about the input parameters to be modeled. Based on the model assumptions and data, it is concluded that the prevalence of VTEC O157 in meat products (joints and mince) at consumption is low (i.e.

A farm-to-consumption quantitative microbiological risk assessment (QMRA) for Salmonella in pigs in the European Union has been developed for the European Food Safety Authority. The primary aim of the QMRA was to assess the impact of hypothetical reductions of slaughter-pig prevalence and the impact of control measures on the risk of human Salmonella infection. A key consideration during the QMRA development was the characterization of variability between E.U. Member States (MSs), and therefore a generic MS model was developed that accounts for differences in pig production, slaughterhouse practices, and consumption patterns. To demonstrate the parameterization of the model, four case study MSs were selected that illustrate the variability in production of pork meat and products across MSs. For the case study MSs the average probability of illness was estimated to be between 1 in 100,000 and 1 in 10 million servings given consumption of one of the three product types considered (pork cuts, minced meat, and fermented ready-to-eat sausages). Further analyses of the farm-to-consumption QMRA suggest that the vast majority of human risk derives from infected pigs with a high concentration of Salmonella in their feces (≥10(4) CFU/g). Therefore, it is concluded that interventions should be focused on either decreasing the level of Salmonella in the feces of infected pigs, the introduction of a control step at the abattoir to reduce the transfer of feces to the exterior of the pig, or a control step to reduce the level of Salmonella on the carcass post-evisceration.

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.

We propose stage-structured population models for species whose adult members are subject to culling, with a view to understanding the culling regimes that are likely to result in eradication of the species. A purely time-dependent model is proposed in which culling occurs at particular discrete times, not necessarily equally spaced. Then a reaction-diffusion model is proposed for a situation in which the adults can diffuse; in this model the culling is continuous in time but occurs only at particular discrete points in space. Such a model might be appropriate for pheromone trapping of insects. For both models conditions are obtained that are sufficient for species eradication.

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.

The burden of Salmonella entering pig slaughterhouses across the European Union is considered a primary food safety concern. To assist E.U. member states with the development of national control plans, we have developed a farm transmission model applicable to all member states. It is an individual-based stochastic susceptible-infected model that takes into account four different sources of infection of pigs (sows, feed, external contaminants such as rodents, and new stock) and various management practices linked to Salmonella transmission/protection (housing, flooring, feed, all-in-all-out production). A novel development within the model is the assessment of dynamic shedding rates. The results of the model, parameterized for two case study member states (one high and one low prevalence) suggest that breeding herd prevalence is a strong indicator of slaughter pig prevalence. Until a member state's' breeding herd prevalence is brought below 10%, the sow will be the dominant source of infection to pigs raised for meat production; below this level of breeding herd prevalence, feed becomes the dominant force of infection.

Bat-borne viruses have been linked to a number of zoonotic diseases; in 2014 there have been human cases of Nipah virus (NiV) in Bangladesh and Ebola virus in West and Central Africa. Here we describe a model designed to provide initial quantitative predictions of the risk of entry of such viruses to European Union (EU) Member States (MSs) through four routes: human travel, legal trade (e.g. fruit and animal products), live animal movements and illegal importation of bushmeat. The model utilises available datasets to assess the movement via these routes between individual countries of the world and EU MSs. These data are combined with virus specific data to assess the relative risk of entry between EU MSs. As a case study, the model was parameterised for NiV. Scenario analyses showed that the selection of exporting countries with NiV and potentially contaminated trade products were essential to the accuracy of all model outputs. Uncertainty analyses of other model parameters identified that the model expected number of years to an introduction event within the EU was highly susceptible to the prevalence of NiV in bats. The relative rankings of the MSs and routes, however, were more robust. The UK, the Netherlands and Germany were consistently the most likely points of entry and the ranking of most MSs varied by no more than three places (maximum variation five places). Legal trade was consistently the most likely route of entry, only falling below human travel when the estimate of the prevalence of NiV in bats was particularly low. Any model-based calculation is dependent on the data available to feed into the model and there are distinct gaps in our knowledge, particularly in regard to various pathogen/virus as well as host/bat characteristics. However, the strengths of this model lie in the provision of relative comparisons of risk among routes and MSs. The potential for expansion of the model to include other routes and viruses and the possibility of rapid parameterisation demonstrates its potential for use in an outbreak situation.

The burden of Salmonella entering pig slaughterhouses across the European Union (EU) is considered to be of public health significance. Therefore, targets will be set for each EU Member State (MS) to reduce the prevalence of Salmonella infection in pigs at slaughter. In order to meet the set target, each MS will need to develop a National Control Plan (NCP). As part of the evidence base for the development of NCPs, a Quantitative Microbiological Risk Assessment (QMRA) was funded under an Article 36 grant to support the scientific opinion required by the EC from the European Food Safety Authority (EFSA) and andopted by the BIOHAZ panel.

A farm-to-consumption quantitative microbiological risk assessment (QMRA) for Salmonella in pigs has been developed for the European Food Safety Authority. The primary aim of the QMRA was to assess the impact of reductions of slaughterpig prevalence and the impact of important control measures applied at the farm and during transport, lairage and slaughter on the number of human cases of salmonellosis. The QMRA estimates the risk of salmonellosis and number of human cases for three product types: pork cuts, minced meat and fermented ready-to-eat sausages. For four case study European Union Member States (MSs) the average probability of illness was estimated to be between 1 in 100,000 and 1 in 10 million servings given consumption of one of the three product types. The total numbers of cases attributable to the three product types was also estimated. The results from the intervention analysis suggest that specific slaughterhouse interventions are currently best placed to produce consistently large reductions in the number of human cases and that for high breeding prevalence MSs reducing infection on breeder farms would seem to be an important on-farm control measure.

Pigs are commonly infected with Salmonella spp. at the slaughterhouse, and the consumption of pig meat is hypothesised to be an important contributor to human salmonellosis. The European Union (EU) will shortly set targets for the reduction of Salmonella in pigs at slaughter for each Member State (MS), and each MS is expected to put in place a National Control Plan (NCP) in order to achieve their targets. If MSs are to realise their targets then practical interventions that consistently work must be identified. As part of the evidence base for the development of NCPs, a Quantitative Microbiological Risk Assessment (QMRA) was funded to support the scientific opinion required by the EC from the European Food Safety Authority, which was subsequently adopted by the BIOHAZ panel. Here we describe how the baseline model was modified to simulate the effect of both on-farm and abattoir interventions, and the resultant reductions on the predicted number of human Salmonella cases attributable to pig meat consumption in an EU MS. We present the results from two case study MSs with differing slaughter pig Salmonella prevalence and production systems to exemplify the differences that interventions have between MSs. In the two MSs, both on-farm and abattoir interventions were predicted to be able to produce a significant reduction in salmonellosis attributable to pig meat consumption. Anal bunging of the carcass during processing was shown to be the most effective intervention mechanism in reducing human illness. Some intervention mechanisms were ineffective in reducing human illness, including increased cleaning and disinfection (CD Mousing et al. 1997); however, the success of these programs is varied (Nielsen et al. 2001, BPEx 2009), In addition, it is not a easy task to attribute reductions in human cases to a control program due to the natural variation in foodborne cases that would occur regardless of any intervention. If MSs are to meet their targets, and the EU is to realise its aim of reducing human salmonellosis attributable to pig meat consumption, then practical interventions (across the food chain) that work consistently and efficiently must be identified. The most effective interventions are likely to differ between MSs, due to factors such as differing management practices or production processes involved in producing a MS’s commonly consumed pig meat products. Therefore, as part of the evidence base for the development of NCPs in individual MSs, a QMRA was funded to support the scientific opinion required by the EC from the European Food Safety Authority. As such, the main aim of this QMRA was to assess the effectiveness of interventions implemented on-farm and at the abattoir and how this may vary across EU MSs. Therefore the baseline model (EFSA 2010) was modified to describe the effect of both on-farm and abattoir interventions and the resultant reductions (if any) on the predicted number of human Salmonella cases in an EU MS attributable to the consumption of 3 pig meat products (pork chops, minced meat and fermented sausage). We present the results from two case

International audience; The transmission of antimicrobial resistance (AMR) between animals, their environment, food and humans is a complex issue. Previous pharmacokinetic-pharmacodynamic (PKPD) models indicate that extended-spectrum β-lactamase (ESBL) resistant bacterial populations may be self-sustaining through horizontal and vertical gene transfer, even in the absence of antimicrobial pressure. However, models focusing purely on the biochemical aspects fail to incorporate the complicated host population dynamics which occur within a farm environment. Models of disease transmission within commercial farm environments can provide further insight to the on-farm transmission dynamics of AMR between animals and their environment, as well as predict the effect of various on-farm interventions. Here, we present a risk assessment which predicts the likelihood that slaughter-aged pigs would carry resistant bacteria after a single introduction of ESBL E. coli on commercial pig farms. We incorporate outputs from a PKPD model which explores the complex host/gastrointestinal bacteria interplay after antimicrobial treatment; with an on-farm model of bacterial transmission. The risk assessment is designed to be adaptable for the simultaneous transmission of multiple bacteria and resistant strains. We predicted that after introduction onto a pig farm, ESBL E. coli bacteria are likely to persist on the farm for more than a year, leading to a high batch prevalence (39.4% slaughter pigs, 5th and 95th percentiles: 0.0–57.5) and high faecal shedding. A comparison of different farm management types suggested that all-in-all-out housing was a protective measure for both prevalence in slaughter-aged pigs and faecal shedding rates. We applied two main interventions at the farm level, an enhanced cleaning and disinfectant (C&D) protocol and isolation of pigs in sick pens for the duration of their antibiotic treatment. Both interventions were able to reduce the number of pigs shedding more than 2 log10 ESBL E. coli from 18.7% (5th and 95th percentiles: 5.9–30.4) in the baseline scenario, to 7.2% (5th and 95th percentiles: 0.0–21.5) when an enhanced C&D protocol was applied, 0.1% (5th and 95th percentiles: 0.0–0.3) when sick pens were used and 0.1% (5th and 95th percentiles: 0.0–0.3) when a combination of enhanced C&D plus sick pens was used. Both scenarios also reduced the prevalence in batches of pigs going to slaughter. This effect was largest when sick pens were used, where 75% of batches had 0% positive pigs. The results suggest that a single introductory event is sufficient to cause a substantial risk of carriage in slaughter-aged pigs. Further quantitative microbial risk assessments (QMRA) are needed to consider the onwards risk posed to later parts of the food chain.