Safeguarding global health is a moving target, with changes in climate and human activity raising new threats to public health, animal health and animal welfare. Decision support in the context of health management requires tools capable of making sense of large volumes of complex and multidisciplinary evidence. A One Health living risk assessment tool, L'ORA, was developed to assess the incursion risk for zoonotic and animal diseases using a One Health approach and allowing for automatic updates of the risk assessment. L'ORA estimates the incursion risk as the resultant of four steps: 1) Disease occurrence in source areas based on the distribution of diseases worldwide; 2) Rate of incursion based on the individuals or products moved from source areas to target areas and their probability of infection or contamination and contact with susceptible hosts in target areas; 3) Extent of disease spread in target areas, considering both domestic and wild host populations, vectors if the disease is vector-borne, and humans if the disease is zoonotic; and 4) Impact of the disease outbreak and concurrent control measures on public health, animal health and animal welfare, accounting for economic, societal, and environmental impacts. L'ORA is a decision support tool that was built as a generic tool comparing the relative risks of multiple diseases across multiple target areas. L'ORA monthly evaluates the incursion risk for each NUTS2 region in the European Union. Results can be used to identify areas of greater risk for each of the diseases to inform risk managers on where to focus attention and resources, and to inform, e.g., risk-based surveillance.

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

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

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

The novel coronavirus SARS-CoV-2 likely emerged from a wildlife source with transmission to humans followed by rapid geographic spread throughout the globe and severe impacts on both human health and the global economy. Since the onset of the pandemic, there have been many instances of human-to-animal transmission involving companion, farmed and zoo animals, and limited evidence for spread into free-living wildlife. The establishment of reservoirs of infection in wild animals would create significant challenges to infection control in humans and could pose a threat to the welfare and conservation status of wildlife. We discuss the potential for exposure, onward transmission and persistence of SARS-CoV-2 in an initial selection of wild mammals (bats, canids, felids, mustelids, great apes, rodents and cervids). Dynamic risk assessment and targeted surveillance are important tools for the early detection of infection in wildlife, and here we describe a framework for collating and synthesising emerging information to inform targeted surveillance for SARS-CoV-2 in wildlife. Surveillance efforts should be integrated with information from public and veterinary health initiatives to provide insights into the potential role of wild mammals in the epidemiology of SARS-CoV-2.

EDITORIAL article Front. Microbiol., 26 November 2021 | https://doi.org/10.3389/fmicb.2021.795098

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.

African 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 (<20km on average). We also predict what the relative success of different control strategies would have been in 2015, if they were implemented in 2014. Results suggest that hunting of boar reduces the number of new cases, but a larger region is at risk of ASF compared to no control measure. Alternatively, introducing 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.

AbstractInternational travel poses substantial risks for continued introduction of SARS-CoV-2. As of the 17th August 2020, travellers from 12 of the top 25 countries flying into the UK are required to self-isolate for 14 days. We estimate that 895 (CI: 834–958) infectious travellers arrive in a single week, of which 87% (779, CI: 722–837) originate from countries on the UK quarantine list. We compare alternative measures to the 14 day self-isolation (78.0% effective CI: 74.4–81.6) which could be more feasible long-term. A single RT-PCR taken upon arrival at the airport is 39.6% (CI: 35.2–43.7) effective, or equivalently, it would only detect 2 in 5 infectious passengers. Alternatively, testing four days after arrival is 64.3% (CI: 60.0–68.3) effective whereas a test at the airport plus additional test four days later is 68.9% (CI: 64.9–73.0) effective. Rapidly implementing control measures for travellers from risky countries is vital to protect public health; this methodology can be quickly updated to assess the impact of any further changes to international travel policy or disease occurrence.

AbstractSince the identification of Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019, there have been more than 17 million cases of the disease in 216 countries worldwide. Comparisons of prevalence estimates between different communities can inform policy decisions regarding safe travel between countries, help to assess when to implement (or remove) disease control measures and identify the risk of over-burdening healthcare providers. Estimating the true prevalence can, however, be challenging because officially reported figures are likely to be significant underestimates of the true burden of COVID-19 within a community. Previous methods for estimating the prevalence fail to incorporate differences between populations (such as younger populations having higher rates of asymptomatic cases) and so comparisons between, for example, countries, can be misleading. Here, we present an improved methodology for estimating COVID-19 prevalence. We take the reported number of cases and deaths (together with population size) as raw prevalence for the population. We then apply an age-adjustment to this which allows the age-distribution of that population to influence the case-fatality rate and the proportion of asymptomatic cases. Finally, we calculate the likely underreporting factor for the population and use this to adjust our prevalence estimate further. We use our method to estimate the prevalence for 166 countries (or the states of the United States of America, hereafter referred to as US state) where sufficient data were available. Our estimates show that as of the 30th July 2020, the top three countries with the highest estimated prevalence are Brazil (1.26%, 95% CI: 0.96 – 1.37), Kyrgyzstan (1.10%, 95% CI: 0.82 – 1.19) and Suriname (0.58%, 95% CI: 0.44 – 0.63). Brazil is predicted to have the largest proportion of all the current global cases (30.41%, 95%CI: 27.52 – 30.84), followed by the USA (14.52%, 95%CI: 14.26 – 16.34) and India (11.23%, 95%CI: 11.11 – 11.24). Amongst the US states, the highest prevalence is predicted to be in Louisiana (1.07%, 95% CI: 1.02 – 1.12), Florida (0.90%, 95% CI: 0.86 – 0.94) and Mississippi (0.77%, 95% CI: 0.74 – 0.81) whereas amongst European countries, the highest prevalence is predicted to be in Montenegro (0.47%, 95% CI: 0.42 - 0.50), Kosovo (0.35%, 95% CI: 0.29 - 0.37) and Moldova (0.28%, 95% CI: 0.23 - 0.30). Our results suggest that Kyrgyzstan (0.04 tests per predicted case), Brazil (0.04 tests per predicted case) and Suriname (0.29 tests per predicted case) have the highest underreporting out of the countries in the top 25 prevalence. In comparison, Israel (34.19 tests per predicted case), Bahrain (19.82 per predicted case) and Palestine (9.81 tests per predicted case) have the least underreporting. The results of this study may be used to understand the risk between different geographical areas and highlight regions where the prevalence of COVID-19 is increasing most rapidly. The method described is quick and easy to implement. Prevalence estimates should be updated on a regular basis to allow for rapid fluctuations in disease patterns.

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 km(2) 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 forWestern 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.

Abstract An increase in confirmed human salmonellosis cases in the EU after 2014 triggered investigation of contributory factors and control options in poultry production. Reconsideration of the five current target serovars for breeding hens showed that there is justification for retaining Salmonella Enteritidis, Salmonella Typhimurium (including monophasic variants) and Salmonella Infantis, while Salmonella Virchow and Salmonella Hadar could be replaced by Salmonella Kentucky and either Salmonella Heidelberg, Salmonella Thompson or a variable serovar in national prevalence targets. However, a target that incorporates all serovars is expected to be more effective as the most relevant serovars in breeding flocks vary between Member State (MS) and over time. Achievement of a 1% target for the current target serovars in laying hen flocks is estimated to be reduced by 254,400 CrI95[98,540; 602,700] compared to the situation in 2016. This translates to a reduction of 53.4% CrI95[39.1; 65.7] considering the layer‐associated human salmonellosis true cases and 6.2% considering the overall human salmonellosis true cases in the 23 MSs included in attribution modelling. A review of risk factors for Salmonella in laying hens revealed that overall evidence points to a lower occurrence in non‐cage compared to cage systems. A conclusion on the effect of outdoor access or impact of the shift from conventional to enriched cages could not be reached. A similar review for broiler chickens concluded that the evidence that outdoor access affects the occurrence of Salmonella is inconclusive. There is conclusive evidence that an increased stocking density, larger farms and stress result in increased occurrence, persistence and spread of Salmonella in laying hen flocks. Based on scientific evidence, an impact of Salmonella control programmes, apart from general hygiene procedures, on the prevalence of Campylobacter in broiler flocks at the holding and on broiler meat at the end of the slaughter process is not expected.

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 circular economy relies on demonstrating the quality and environmental safety of wastes that are recovered and reused as products. Policy-level risk assessments, using generalised exposure scenarios, and informed by stakeholder communities have been used to appraise the acceptability of necessary changes to legislation, allowing wastes to be valued, reused and marketed. Through an extensive risk assessment exercise, summarised in this paper, we explore the burden of proof required to offer safety assurance to consumer and brand-sensitive food sectors in light of attempts to declassify, as wastes, quality-assured, source-segregated compost and anaerobic digestate products in the United Kingdom. We report the residual microbiological and chemical risks estimated for both products in land application scenarios and discuss these in the context of an emerging UK bioeconomy worth 52bn pound per annum. Using plausible worst case assumptions, as demanded by the quality food sector, risk estimates and hazard quotients were estimated to be low or negligible. For example, the human health risk of E. coli 0157 illness from exposure to microbial residuals in quality-assured composts, through a ready-to-eat vegetable consumption exposure route, was estimated at similar to 10(-8) per person per annum. For anaerobic digestion residues, 7 x 10(-3) cases of E. coli 0157 were estimated per annum, a potential contribution of 0.0007% of total UK cases. Hazard quotients for potential chemical contaminants in both products were insufficient in magnitude to merit detailed quantitative risk assessments. Stakeholder engagement and expert review was also a substantive feature of this study. We conclude that quality-assured, source-segregated products applied to land, under UK quality protocols and waste processing standards, pose negligible risks to human, animal, environmental and crop receptors, providing that risk management controls set within the standards and protocols are adhered to.

The European Food Safety Authority (EFSA) asked the Panel on Biological Hazards (BIOHAZ) to deliver a scientific Opinion on the maintenance of the list of qualified presumption of safety (QPS) biological agents. The QPS approach was developed by the EFSA Scientific Committee to provide a harmonised generic pre-evaluation to support safety risk assessments of biological agents intentionally introduced into the food and feed chain, in support of the concerned scientific Panels and Units in the frame of market authorisations. The taxonomic identity, body of knowledge, safety and antimicrobial resistance of biological agents are assessed. Safety concerns identified for the respective taxonomic unit (TU) are, where possible and reasonable in number, reflected as ‘qualifications’ which should be assessed at the strain level by the EFSA’s scientific Panels. The list of QPS status recommended biological agents, first established in 2007, has been continuously revised and updated through a scientific Opinion but since 2014 the evaluation is undertaken every 3 years and no longer carried out annually. If new information is retrieved from extended literature searches that would change the QPS status of a TU or its qualifications, this is published in the Panel Statement. The Panel Statement also includes the evaluation of microbiological agents notified to EFSA within the 6-month period for an assessment for feed additives, food enzymes, food additives and flavourings, novel foods or plant protection products. In result of each Panel Statement, the ‘2016 updated list of QPS status recommended biological agents for safety risk assessments carried out by EFSA scientific Panels and Units’ is updated with the inclusion of new recommendations for QPS status and is appended to the 2016 QPS scientific opinion.

The European Commission asked EFSA for a scientific opinion on chronic wasting disease in two parts. Part one, on surveillance, animal health risk-based measures and public health risks, was published in January 2017. This opinion (part two) addresses the remaining Terms of Reference, namely, ‘are the conclusions and recommendations in the EFSA opinion of June 2004 on diagnostic methods for chronic wasting disease still valid? If not, an update should be provided’, and ‘update the conclusions of the 2010 EFSA opinion on the results of the European Union survey on chronic wasting disease in cervids, as regards its occurrence in the cervid population in the European Union’. Data on the performance of authorised rapid tests in North America are not comprehensive, and are more limited than those available for the tests approved for statutory transmissible spongiform encephalopathies surveillance applications in cattle and sheep. There are no data directly comparing available rapid test performances in cervids. The experience in Norway shows that the Bio-Rad TeSeE SAP test, immunohistochemistry and western blotting have detected reindeer, moose and red deer cases. It was shown that testing both brainstem and lymphoid tissue from each animal increases the surveillance sensitivity. Shortcomings in the previous EU survey limited the reliability of inferences that could be made about the potential disease occurrence in Europe. Subsequently, testing activity in Europe was low, until the detection of the disease in Norway, triggering substantial testing efforts in that country. Available data neither support nor refute the conclusion that chronic wasting disease does not occur widely in the EU and do not preclude the possibility that the disease was present in Europe before the survey was conducted. It appears plausible that chronic wasting disease could have become established in Norway more than a decade ago. © 2018 European Food Safety Authority. EFSA Journal published by John Wiley and Sons Ltd on behalf of European Food Safety Authority.

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