A user-friendly decision support tool to assist one-health risk assessors.

One-Health risk assessments are integral to developing efficient responses to disease threats, including global pandemics. However, short timeframes, inadequate disease-specific information and an insufficient skill-base make it hard for inexperienced assessors to distinguish between a large portfolio of approaches. The wrong choice can detract from the disease response. Here, we present an interactive decision support tool to help with this choice. A workshop with participants from diverse professional backgrounds provided six themes that should be considered when deciding on the best approach. Questions based on these themes were then developed to populate a decision tree which guides users to their most appropriate approach. One-Health risk assessment tools and literature were used as examples of the different approaches. The tool provides links to these examples and short descriptions of the approaches. Answers are easily changed, facilitating exploration though different approaches. The simple data structure of the tool means it is easy to update with more resources and approaches. It provides a valuable source of guidance and information for less experienced risk assessors.

Predicting spread and effective control measures for African swine fever-Should we blame the boars?

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 (<20 km on average). We also predict the relative success of different control strategies in 2015, if they were implemented in 2014. Results suggest that hunting of wild boar reduces the number of new cases, but a larger region is at risk of ASF compared with no control measure. Alternatively, introducing wild boar-proof fencing reduces the size of the region at risk in 2015, but not the total number of cases. Overall, our model suggests wild boar movement is only responsible for local transmission of disease; thus, other pathways are more dominant in medium- and long-distance spread of the disease.

Using network analysis to identify seasonal patterns and key nodes for risk-based surveillance of pig diseases in Italy.

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.

Cross-Validation of Generic Risk Assessment Tools for Animal Disease Incursion Based on a Case Study for African Swine Fever.

In recent years, several generic risk assessment (RA) tools have been developed that can be applied to assess the incursion risk of multiple infectious animal diseases allowing for a rapid response to a variety of newly emerging or re-emerging diseases. Although these tools were originally developed for different purposes, they can be used to answer similar or even identical risk questions. To explore the opportunities for cross-validation, seven generic RA tools were used to assess the incursion risk of African swine fever (ASF) to the Netherlands and Finland for the 2017 situation and for two hypothetical scenarios in which ASF cases were reported in wild boar and/or domestic pigs in Germany. The generic tools ranged from qualitative risk assessment tools to stochastic spatial risk models but were all parameterized using the same global databases for disease occurrence and trade in live animals and animal products. A comparison of absolute results was not possible, because output parameters represented different endpoints, varied from qualitative probability levels to quantitative numbers, and were expressed in different units. Therefore, relative risks across countries and scenarios were calculated for each tool, for the three pathways most in common (trade in live animals, trade in animal products, and wild boar movements) and compared. For the 2017 situation, all tools evaluated the risk to the Netherlands to be higher than Finland for the live animal trade pathway, the risk to Finland the same or higher as the Netherlands for the wild boar pathway, while the tools were inconclusive on the animal products pathway. All tools agreed that the hypothetical presence of ASF in Germany increased the risk to the Netherlands, but not to Finland. The ultimate aim of generic RA tools is to provide risk-based evidence to support risk managers in making informed decisions to mitigate the incursion risk of infectious animal diseases. The case study illustrated that conclusions on the ASF risk were similar across the generic RA tools, despite differences observed in calculated risks. Hence, it was concluded that the cross-validation contributed to the credibility of their results.

Using species distribution models to predict potential hot-spots for Rift Valley Fever establishment in the United Kingdom.

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.

The Risk of Infection by African Swine Fever Virus in European Swine Through Boar Movement and Legal Trade of Pigs and Pig Meat.

African swine fever (ASF) is currently spreading westwards throughout Europe and eastwards into China, with cases occurring in both wild boar and domestic pigs. A generic risk assessment framework is used to determine the probability of first infection with ASF virus (ASFV) at a fine spatial scale across European Union Member States. The framework aims to assist risk managers across Europe with their ASF surveillance and intervention activities. Performing the risk assessment at a fine spatial scale allows for hot-spot surveillance, which can aid risk managers by directing surveillance or intervention resources at those areas or pathways deemed most at risk, and hence enables prioritization of limited resources. We use 2018 cases of ASF to estimate prevalence of the disease in both wild boar and pig populations and compute the risk of initial infection for 2019 at a 100 km2 cell resolution via three potential pathways: legal trade in live pigs, natural movement of wild boar, and legal trade in pig meat products. We consider the number of pigs, boar and amount of pig meat entering our area of interest, the prevalence of the disease in the origin country, the probability of exposure of susceptible pigs or boar in the area of interest to introduced infected pigs, boar, or meat from an infected pig, and the probability of transmission to susceptible animals. We provide maps across Europe indicating regions at highest risk of initial infection. Results indicate that the risk of ASF in 2019 was predominantly focused on those regions which already had numerous cases in 2018 (Poland, Lithuania, Hungary, Romania, and Latvia). The riskiest pathway for ASFV transmission to pigs was the movement of wild boar for Eastern European countries and legal trade of pigs for Western European countries. New infections are more likely to occur in wild boar rather than pigs, for both the pig meat and wild boar movement pathways. Our results provide an opportunity to focus surveillance activities and thus increase our ability to detect ASF introductions earlier, a necessary requirement if we are to successfully control the spread of this devastating disease for the pig industry.

Assessing the time taken for a surveillance system to detect a re-emergence of bovine spongiform encephalopathy in cattle.

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) animals >30months, and all emergency slaughter and fallen stock animals tested when >24months. 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 (majority <24months) 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 animals >72months reduced the time to detection by one year compared to the baseline model, but would incur a high financial cost, e.g. testing HS animals >72months 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.

Application of a quantitative entry assessment model to compare the relative risk of incursion of zoonotic bat-borne viruses into European Union Member States.

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.

A Generic Quantitative Risk Assessment Framework for the Entry of Bat-Borne Zoonotic Viruses into the European Union.

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.

A Quantitative Microbiological Risk Assessment for Salmonella in Pigs for the European Union.

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.

Characterization of the Human Risk of Salmonellosis Related to Consumption of Pork Products in Different E.U. Countries Based on a QMRA.

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.

A Farm Transmission Model for Salmonella in Pigs, Applicable to E.U. Member States.

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.

Potential for introduction of bat-borne zoonotic viruses into the EU: a review.

Bat-borne viruses can pose a serious threat to human health, with examples including Nipah virus (NiV) in Bangladesh and Malaysia, and Marburg virus (MARV) in Africa. To date, significant human outbreaks of such viruses have not been reported in the European Union (EU). However, EU countries have strong historical links with many of the countries where NiV and MARV are present and a corresponding high volume of commercial trade and human travel, which poses a potential risk of introduction of these viruses into the EU. In assessing the risks of introduction of these bat-borne zoonotic viruses to the EU, it is important to consider the location and range of bat species known to be susceptible to infection, together with the virus prevalence, seasonality of viral pulses, duration of infection and titre of virus in different bat tissues. In this paper, we review the current scientific knowledge of all these factors, in relation to the introduction of NiV and MARV into the EU.

Attribution of human VTEC O157 infection from meat products: a quantitative risk assessment approach.

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., <0.04%). Beef products, particularly beef burgers, present the highest estimated risk with an estimated eight out of 100,000 servings on average resulting in human infection with VTEC O157.