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

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

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

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

Assessing the quality of data for drivers of disease emergence.

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.

Les facteurs d'émergence sont des éléments ayant le potentiel direct ou indirect d'influencer la probabilité d'émergence ou de réémergence d'une maladie infectieuse. Il est probablement rare qu'une maladie infectieuse émergente apparaisse en raison d'un seul facteur ; c'est plutôt un faisceau de sous-facteurs (éléments pouvant avoir une influence sur un même facteur) qui contribue à ce que les conditions soient réunies pour qu'un agent pathogène puisse (ré)émerger et s'établir. Les concepteurs de modèles ont donc utilisé les données relatives aux sous-facteurs pour identifier les zones sensibles où les prochaines maladies infectieuses émergentes pourraient survenir, ou pour faire une estimation des sous-facteurs ayant la plus grande influence sur la probabilité de leur occurrence. Les chercheurs ont besoin de données de qualité pour décrire ces sous-facteurs, afin de minimiser le risque d'erreur et de biais lors de la modélisation de l'interaction entre les différents sous-facteurs, et de contribuer ainsi à mieux prédire la probabilité d'apparition d'une maladie infectieuse émergente. Les auteurs présentent une étude de cas qui a consisté à évaluer la qualité des données disponibles relatives aux sous-facteurs d'émergence du virus de la fièvre de West Nile au regard de différents critères. Il est apparu que la qualité des données était variable au regard des critères examinés. Le paramètre dont le score était le plus bas est celui de la complétude - le fait que suffisamment de données soient disponibles pour répondre à toutes les exigences du modèle. Il s'agit pourtant d'un paramètre important car des données incomplètes peuvent inciter à tirer des conclusions erronées des études de modélisation. La disponibilité de données de bonne qualité est essentielle pour réduire l'incertitude lors de l'estimation de la probabilité d'apparition de maladies infectieuses émergentes dans des zones déterminées, ainsi que pour identifier les points critiques de concrétisation du risque où des mesures préventives pourraient être mises en place.

Los inductores o factores de inducción [drivers] son aquellos que, directa o indirectamente, pueden influir en la probabilidad de que surjan o resurjan enfermedades infecciosas. Todo indica que rara vez una enfermedad infecciosa emergente aparece por efecto de un solo factor de inducción, sino que es probable que haya más bien una combinación de "subfactores de influencia" [sub-drivers] (factores que pueden influir en un inductor) que cree condiciones propicias para que un patógeno (re)surja y logre asentarse. Los creadores de modelos, por consiguiente, se han servido de datos sobre estos subfactores de influencia para localizar aquellas zonas donde con mayor probabilidad puedan aparecer próximamente enfermedades infecciosas emergentes o para determinar cuáles son los subfactores que más influyen en la probabilidad de que ello ocurra. Para reducir al mínimo los errores y sesgos al modelizar la interacción entre los subfactores y ayudar así a calcular la probabilidad de que surja una enfermedad infecciosa emergente, los investigadores necesitan datos de buena calidad para caracterizar estos subfactores. En el análisis expuesto por los autores se utilizó el virus del Nilo Occidental como ejemplo de estudio para evaluar, con arreglo a diversos criterios, la calidad de los datos existentes sobre los subfactores que inciden en la aparición de este virus. Lo que se constató, en relación con el grado de cumplimiento de los criterios, es que esos datos eran de calidad variable. La característica o parámetro que deparó la puntuación más baja fue la completud, es decir, la existencia de datos suficientes para aportar al modelo toda la información requerida para que este funcione bien. Se trata de una característica importante, pues un conjunto incompleto de datos podría llevar a extraer conclusiones erróneas de los estudios de modelización. Por ello, para reducir la incertidumbre a la hora de calcular la probabilidad de que en cierto lugar surjan brotes de enfermedades infecciosas emergentes y de determinar, dentro de la cadena de materialización del riesgo, aquellos eslabones en los que cabe adoptar medidas preventivas, es indispensable disponer de datos de buena calidad.

A qualitative risk assessment of cleansing and disinfection requirements after an avian influenza outbreak in commercial poultry.

1. During an avian influenza (AI) outbreak in the United Kingdom, the joint aim of the poultry industry and the Government is to eliminate and prevent the spread of infection, through control measures based on the current European Union (EU) Council Directive (2005/94/EC). An essential part of these measures is the cleansing and disinfection (C&D) of infected premises.2. This risk assessment assessed the differences in re-infection in a repopulated flock if the EU Directive is interpreted to permit secondary C&D to be undertaken either with or without dismantling complex equipment. The assessment estimated the probability of virus survival on different types of equipment in a depopulated contaminated poultry house before and after preliminary and secondary C&D procedures. A risk matrix spreadsheet tool was used to carry out the assessment and concluded that, provided secondary C&D is carried out with due diligence (i.e. carried out to a defined code of practice as agreed by both industry and policymakers), the risk of re-infection from equipment is negligible, both with and without dismantling complex equipment in all farm types considered.3. By considering the equipment types individually, the assessment identified those areas of the house which may still contain viable virus post-preliminary C&D and on which attention should be focussed during secondary C&D. The generic risk pathway and matrix spreadsheet tool have the potential to be used for other pathogens and species, given appropriate data.

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

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

Risk assessment for recrudescence of avian influenza in caged layer houses following depopulation: the effect of cleansing, disinfection and dismantling of equipment.

Following an outbreak of highly pathogenic avian influenza virus (HPAIV) in a poultry house, control measures are put in place to prevent further spread. An essential part of the control measures based on the European Commission Avian Influenza Directive 2005/94/EC is the cleansing and disinfection (C&D) of infected premises. Cleansing and disinfection includes both preliminary and secondary C&D, and the dismantling of complex equipment during secondary C&D is also required, which is costly to the owner and also delays the secondary cleansing process, hence increasing the risk for onward spread. In this study, a quantitative risk assessment is presented to assess the risk of re-infection (recrudescence) occurring in an enriched colony-caged layer poultry house on restocking with chickens after different C&D scenarios. The risk is expressed as the number of restocked poultry houses expected before recrudescence occurs. Three C&D scenarios were considered, namely (i) preliminary C&D alone, (ii) preliminary C&D plus secondary C&D without dismantling and (iii) preliminary C&D plus secondary C&D with dismantling. The source-pathway-receptor framework was used to construct the model, and parameterisation was based on the three C&D scenarios. Two key operational variables in the model are (i) the time between depopulation of infected birds and restocking with new birds (TbDR) and (ii) the proportion of infected material that bypasses C&D, enabling virus to survive the process. Probability distributions were used to describe these two parameters for which there was recognised variability between premises in TbDR or uncertainty due to lack of information in the fraction of bypass. The risk assessment estimates that the median (95% credible intervals) number of repopulated poultry houses before recrudescence are 1.2 × 104 (50 to 2.8 × 106), 1.9 × 105 (780 to 5.7 × 107) and 1.1 × 106 (4.2 × 103 to 2.9 × 108) under C&D scenarios (i), (ii) and (iii), respectively. Thus for HPAIV in caged layers, undertaking secondary C&D without dismantling reduces the risk by 16-fold compared to preliminary C&D alone. Dismantling has an additional, although smaller, impact, reducing the risk by a further 6-fold and thus around 90-fold compared to preliminary C&D alone. On the basis of the 95% credible intervals, the model demonstrates the importance of secondary C&D (with or without dismantling) over preliminary C&D alone. However, the extra protection afforded by dismantling may not be cost beneficial in the context of reduced risk of onward spread.

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

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

Identification of a proteinase K resistant protein for use as an internal positive control marker in PrP Western blotting.

The routine use of an internal positive control (IPC) marker could prove useful in the diagnosis of transmissible spongiform encephalopathy (TSE) diseases, particularly in surveillance programmes where large numbers of negative results are reported. Detection of an endogenous IPC protein in a negative sample adds confidence to the correct sample processing throughout the analytical procedure and could avoid the reporting of false negative diagnoses. Proteinase K (PK) resistance is one of the key diagnostic determinants of the disease-associated form of PrP (PrP(Sc)), the only disease-specific macromolecule currently associated with TSE disease. Additional PK resistant proteins, endogenous to TSE-suspect diagnostic tissue samples, were therefore assessed for use as IPC markers in the Western blot diagnosis of BSE and scrapie. Results indicated that, whilst essentially maintaining a standard PrP extraction and detection protocol, a ferritin heavy chain sub-unit of approximately 22kDa, was consistently detected in all PK treated TSE positive and negative tissue samples tested. Its presence in a range of sample types, any of which could be submitted under BSE and scrapie surveillance programmes, confirmed it as a suitable protein for an IPC marker in PrP(Sc) Western blotting.

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

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