First Expert Elicitation of Knowledge on Drivers of Emergence of Bovine Besnoitiosis in Europe

Bovine besnoitiosis (BB) is a chronic and debilitating parasitic disease in cattle caused by the protozoan parasite Besnoitia besnoiti. South European countries are affected and have reported clinical cases of BB. However, BB is considered as emerging in other countries/regions of central, eastern and northern Europe. Yet, data on drivers of emergence of BB in Europe are scarce. In this study, fifty possible drivers of emergence of BB in cattle were identified. A scoring system was developed per driver. Then, the scoring was elicited from eleven recognized European experts to: (i) allocate a score to each driver, (ii) weight the score of drivers within each domain and (iii) weight the different domains among themselves. An overall weighted score was calculated per driver, and drivers were ranked in decreasing order of importance. Regression tree analysis was used to group drivers with comparable likelihoods to play a role in the emergence of BB in cattle in Europe. Finally, robustness testing of expert elicitation was performed for the seven drivers having the highest probability to play a key role in the emergence of BB: i.e., (i) legal/illegal movements of live animals from neighbouring/European Union member states or (ii) from third countries, (iii) risk of showing no clinical sign and silent spread during infection and post infection, (iv) as a consequence, difficulty to detect the emergence, (v) existence of vectors and their potential spread, (vi) European geographical proximity of the pathogen/disease to the country, and (vii) animal density of farms. Provided the limited scientific knowledge on the topic, expert elicitation of knowledge, multi-criteria decision analysis, cluster and sensitivity analyses are very important to prioritize future studies, e.g., the need for quantitative import risk assessment and estimation of the burden of BB to evidence and influence policymaking towards changing (or not) its status as a reportable disease, with prevention and control activities targeting, firstly, the top seven drivers. The present methodology could be applied to other emerging animal diseases.

ically to BB. In 1968, Bigalke demonstrated the possibility of vector control [11]. Some pyrethroids are active on stomoxes, but controlling the latter becomes difficult because of insecticide resistance development [18,19]. For tabanids, only a short time effect of pyrethroid insecticides has been cited [20] and they are considered far less effective than similar applications targeting other vectors (e.g., mosquitoes).
Outbreaks of BB have been reported in Africa, mainly in the South [14]. BB has also been reported in Israel, Kazakhstan, the People's Republic of China, India and Venezuela [21,22]. In Europe, the disease is endemic (referring to a clinically expressed or non-expressed disease that occurs regularly in animals in a given area) in Spain, Portugal, Italy and France [21,23,24]. In endemic areas, there are very few studies on the economic impact of BB. However, recently, a paper has revealed an association with a higher milk somatic cell count and BB, which can induce important economic losses [25].
Several outbreaks have been reported in European non-endemic countries [22] such as Germany [26], Switzerland [27], Croatia [28], Hungary [29], Ireland [30] and Belgium [31,32]. A range of reasons could explain this new scenario, including the animal trade, management practices such as sharing pastures, and climate change by modification of the vector activity [33]. Indeed, disease emergence is related to the joint presence of several factors, called "drivers". The knowledge of these drivers is crucial to properly understand hostpathogen-environment interactions [34].
The aim of this study was to investigate, for the first time, the drivers of emergence of BB in Europe using expert elicitation. Multi-criteria decision analysis (MCDA) was chosen to allow systematic integration of information from a range of sources [35] and improve repeatability and transparency [36].

Response Rate and Field of Expertise Mobilised by the Experts
Eleven European professionals with recognized scientific knowledge and/or field knowledge or experience on BB in cattle were contacted and all agreed to participate. The fields of expertise were summarized in Appendix A Table A1.

Estimating the Overall Weighted Score and Ranking of Drivers of Bovine Besnoitiosis in Cattle
The medians of the weight between domains of drivers as well as for the different drivers were not equal according to the non-parametric Kruskal-Wallis test (Chi-squared test = 30.1 with 7 d.f. and α = 0.05, p-value = 0.0001; and Chi-squared test = 119.1 with 49 d.f. and α = 0.05, p-value = 0.0001, for the weights between domains and weights of the different drivers, respectively) ( Figure 1).
The median of the weight of the domain D6 (wildlife interface) was significantly lower than the median of the other domains (bootstrapped regression; p-value < 0.001).
Ten drivers out of 50 were ranked as having the highest probability to play a key role in the emergence of BB in Europe. Indeed, the following drivers were ranked in a descending order of importance: the most likely influence of (il)legal movements of live animals (i.e., cattle) from neighboring/European Union member states (MS) (D8-4) or Third countries (a country that is not a member of the European Union as well as a country or territory whose citizens do not enjoy the European Union right to free movement) (D8-7) for the disease to (re)emerge in a given country, the risk of showing no clinical sign and silent spread during infection and post infection (D1-5) and as consequence, the difficulty of detecting the emergence (D3-7), the existence of vectors and its potential spread (D1-7), the European geographic proximity of the pathogen/disease to the country (D2-2), the animal density of farms with extensive (small holders with a few animals) versus intensive farming (D4-3), the disease's last reported case in Europe (D2-3), the mode of transmission of the pathogen (D1-8) and the problem of the ability of preventive/control measures to stop the disease from entering the country or spreading, excluding treatment, vaccination and vector(s)/reservoir(s) control (D3-1) (Figure 2). . Legend: The solid bold line represents the median of the score distribution between the different experts; the solid lines below and above each rectangle represent, respectively, the first and the third quartiles; adjacent lines to the whiskers represent the limits of the 95% confidence interval; small circles represent outside values. The eight domains of drivers are: D1, pathogen/disease characteristics; D2, distance of outbreaks (spatial-temporal scales); D3, ability to monitor, treat and control the disease; D4, European farm characteristics; D5, changes in climate conditions; D6, wildlife interface; D7, human activity; and D8, economic and trade activities. Ranking of the median overall weighted score for each potential driver of bovine besnoitiosis in cattle. (Boxplot based on 11 experts). Legend: the X-Axis represents the drivers with the following codification: D1 to D8 refer to the eight domains of drivers and D1_1 to D8_9 refer to a specific driver (for the codification, see Appendix B), small circles represent outside values. The relation to Figure 3 was provided by the group named as having, respectively, "more importance" and "less importance" in bovine besnoitiosis emergence. Aggregation of drivers of bovine besnoitiosis in cattle using the score, into two homogenous groups using a regression tree analysis. Legend: N, number; Average, average score; SD, standard deviation; D1 to D8 refer to the eight domains of drivers and D1_1 to D8_9 refer to a specific driver (for the codification, see Appendix B).

Sensitivity Analysis of the Impact of Experts on the Final Ranking of Bovine Besnoitiosis Top Drivers of Emergence in Cattle
The result of the sensitivity analysis indicated that irrespective of the expert ignored, ignoring an expert only had no effect on the ranking of top 5 or 7 drivers (i.e., drivers included in the cluster with significantly more importance) considering a change of one or two ranks, respectively. These results were confirmed using a Kruskal-Wallis equality-of-populations rank test on experts for the top five or seven drivers identified. Indeed, results were very conclusive and respectively: chi-squared = 9.61 with 10 d.f. and probability = 0.48 (top five drivers) and chi-squared = 11.17 with 10 d.f. and probability = 0.34 (top seven drivers).

Discussion
Fifty drivers of BB in cattle were ranked and aggregated into two homogenous groups according to the present expert elicitation. Only the first ten most important ranked drivers will be further discussed with a focus on the seven categorized in the "more importance" node. In addition, for ranking of the first seven drivers, there was no expert effect when assessed by sensitivity analysis, indicating an acceptable robustness of the elicitation for the seven drivers included in the first node.
The first and second most important drivers were the influence of (il)legal movements of live animals from neighboring/European Union MS (D8-4) or from Third countries (D8-7), respectively. In European countries there are currently fewer movements of live animals originating from Third countries (note that BB was also reported in Africa and Asia) than from neighboring/European Union MS, explaining the difference in rank of these two drivers (see also international trade statistics, available at the following URL address: https://www.trademap.org/tradestat/Index.aspx; accessed on 15 December 2021). However, the animal trade from Africa to Europe may explain why the disease appeared in Europe by the end of 19th century and the beginning of 20th century in Portugal and France. In addition, there are very few studies available on the estimation of illegal movements of live animals in the scientific literature (e.g., [37,38]). Nevertheless, a proper estimation of the relative importance of illegal movements of live animals and their introduction pathways is deemed essential to set up risk-based awareness, prevention and surveillance programs that correspond to reality [34]. Direct (isolation of the protozoan parasite and real-time polymerase chain reaction (rtPCR)) and indirect (IFAT, WB and ELISA) diagnostic tests have been set up for BB [14]. Some commercial assays permit to implement a proper testing strategy in order to control the trade of live animals and to certify the sanitary BB status of the herd of origin. In order to identify mitigation measures, we strongly recommend developing a quantitative import risk assessment (QIRA) modelling similar to those developed for Lumpy skin disease that involved live bovines as well as S. calcitrans as a mechanic vector [39,40]. These previous studies can serve as a basis for further modelling development.
The third and the fourth most important drivers were related to the risk of showing no clinical sign and silent spread during infection and post infection (D1-5) and as consequence, the difficulty to detect the emergence (D3-7). The disease is expressed only in the most susceptible animals [9,41,42]. In the chronic phase, cutaneous lesions and patognomnic scleral cysts may be helpful for diagnosis and also surveillance. However, many animals are sub-clinically infected with low parasite loads and they may act as parasite carriers which can only be diagnosed by serological tools [43]. In endemic areas, clinical cases are observed between 1-10% of the new infections but between 15-20% in the case of B. besnoiti infections in areas where the disease is emerging [44]. Moreover, during the first weeks following infection, acutely infected animals may be difficult to be clinically diagnosed due to non-specific signs [21]. In addition, several other diseases should be considered in the differential diagnosis according to the stage of the BB such as malignant catarrhal fever, bovine granulocytic ehrlichiosis, bluetongue, bovine respiratory disease, photosensitization, scabies or zinc deficiency [13]. Indeed, clinical surveillance of BB is not fully efficient, and it is essential to carry out confirmatory laboratory tests [21]. There is no formal gold standard test for BB but four tests are frequently used to confirm a clinical suspicion of BB: rtPCR and serological tests (IFAT, ELISA and WB). For WB, the sensitivity (Se) and specificity (Sp) are close to 100%. The Se and Sp of the IFAT are close to 100% and 95%, respectively. Depending on the ELISA used, their Se and Sp are generally > 97% and >93%, respectively [45]. The Se and Sp of the rtPCR are around 90% and >99% [46]. A previous study recommended also a mandatory active surveillance system via a systematic analysis of all imported animals originating from areas at risk [32]. Research is recommended to develop more commercial accurate laboratory assays and decision-making trees able to help the diagnostic of BB.
The fifth most important driver was the existence of vectors and their potential spread (D1-7). The knowledge of different species of blood-sucking insects in a country, their distribution and frequency over time, and the time-period during which vectors remain infectious after a blood meal on an infected animal [10][11][12] are of prime importance to develop QIRA modelling [39,40]. In addition, due to the presence of mechanical vectors of BB (i.e., S. calcitrans and tabanids) in Europe, the seasonality of BB was previously reported as playing a major role in disease epidemiology [18]. The inclusion of seasonality should be valuable for further development of a QIRA modelling.
The sixth most important driver was geographic proximity between a specific nonendemic country and a specific endemic country of origin (D2-2). This driver is related to the third and the fourth ones because if a disease is notifiable, it is easier to secure the trade. Threat analysis and QIRA modelling should be appropriate responses to deal with this driver [39,40].
The seventh most important driver is related to the animal density of farms with extensive (small holders with a few animals) versus intensive farming (D4-3). Density of farms is a driver of spread of a disease, especially if mechanical vectors are present and if these vectors are able to transmit parasites for few hours after their last (interrupted) blood meal on infected cattle [12].
The eighth most important driver is the last reported case of the disease in Europe (D2-3). This driver can be related to the fact that BB is currently not a reportable disease in most of the affected countries. Several criteria to include a disease as reportable exist among which the most important is its zoonotic character (that was not the case for BB according to [17]) and its significant health impacts, taking into account the occurrence and severity of the clinical signs, including direct production losses and mortality [47]. Despite several papers reporting economic concerns related to BB (e.g., [28,32]), factual data on the burden of the disease and its translation to monetary losses are completely lacking [17]. We strongly recommend estimating the economic burden of BB in order to convince policy makers to take action (or not) whether to include BB as a notifiable disease based on factual data.
The ninth most important driver was related to the mode of transmission of the pathogen (D1-8). There is evidence that several biting insects can mechanically transmit B. besnoiti [8][9][10][11] but the entire life cycle remains unknown and especially the definitive host [6]. The intra-herd transmission of BB is generally intense but weak between herds [48]. However, no information of the basic reproductive number for BB is known. Currently, in Europe, there is no strong evidence of the role of the wildlife in BB [49,50]. More studies are needed.
The tenth most important driver was related to the problem of the ability of preventive/control measures to stop the disease from entering the country or spreading (D2-1). Recent studies recommended the awareness of decision-makers about the need for an appropriate prevention and control policy, law enforcement and the implementation of necessary measures to avoid BB becoming endemic in non-endemic countries [32,[51][52][53]. As biosecurity measures, a quarantine and a systematic screening of all imported animals originating from areas at risk can be proposed [32]. In addition, in South Africa and Israel, live-attenuated vaccines were used [17]. Other valuable preventive/control measures should be identified using networking permitting sharing of information and experiences between researchers/veterinarians and literature search, especially systematic review and meta-analyses and using an evidenced-based approach.
As an example of a recent advance, real-time PCR on skin biopsies permitted the detection of super-spreaders in BB [1] and identification/elimination of these super-spreaders contribute to disease control in heavily infected herds. The control of stable flies can be difficult by the development of insecticide resistance [19] and nothing is known about the eventual resistance of horseflies to insecticides (or even their effectiveness). In addition, the lack of repellents with long lasting activity in livestock hampers ecto-parasite control. Moreover, regular treatments are not feasible in extensive husbandry systems.
Considering the European spread in time and space of the BB, the importance of live-animal trade between European countries (endemic versus non-endemic), the fact that notification of the disease is currently not mandatory, the large proportion of sub-clinically infected animals (but at risk), the need for affordable confirmatory tests, and the climatic changes that affect and alter the habitats and population dynamics of vectors, the BB is becoming a concern and needs more collective efforts to limit its spread and its impacts.

Materials and Methods
The methodology followed in this expert elicitation of knowledge is the same as previously published [33,34] for other emerging diseases but is adapted for BB. For transparency, the method is detailed below.

Species Included
The objective was to prioritize the drivers of BB in Europe. Using the following algorithms

Questionnaire Design
To determine the main drivers of BB emergence, a questionnaire was used. A driver was defined as a factor that has the potential to directly or indirectly precipitate ("drive") or lead to the emergence of BB in cattle. A former questionnaire made to rank (re-)emergence of animal diseases based on drivers [33] was adjusted for bovine besnoitiosis in cattle. These were formatted in an Excel ® (Microsoft, Redmond, WA, USA, 2016) file with one spreadsheet per domain, each domain harbouring its respective drivers. Each driver had a score with its definition, which could range from 0 to 4 or 1 to 4 and an intra driver weight point. A last spreadsheet was added, in which the 8 domains were listed, with an inter-domain weight.

Expert Elicitation on Drivers Used to Assess the Emergence of Bovine Besnoitiosis in Europe
An expert elicitation of knowledge was conducted, which consisted of gathering the opinion of people with recognized scientific expertise and/or experience in the field of BB in cattle (Appendix A). For guidance purposes, an explanatory letter accompanied the questionnaire that each expert had to fill out (Appendix C). Each expert was contacted personally and responded individually to the questionnaire. Data generated by the elicitation were based on the individual values provided by experts in order to capture the degree of variability of experts' knowledge. The elicitation was performed in one month.

Scoring and Weighting System
The elicited experts were asked to provide three types of information. First, they were asked to score the drivers (as established in Appendix B). For each driver, the higher the score, the higher the driver's chance to contribute to the emergence of BB in cattle. Uncertainty score was not asked due to lack of evidence-based data on BB in cattle at this stage. Secondly, experts were requested to weight each driver within a specific domain (intra-domain weight). This relative weight was determined using the Las Vegas technique [54]. Briefly, experts were given a number of points to be distributed between the drivers according to their importance in the specific domain. If all the drivers of a given domain had been considered as equivalent by experts, each of them would have received the same score. Lastly, the relative importance of each domain was subsequently weighted by experts (inter-domain weight).

Calculation of an Overall Weighted Score for Each Driver and Ranking Process
To obtain the overall score per driver, an aggregation method that combined the two types of weighting (i.e., the intra-and inter-domain) was used. First, the driver score (coefficients attributed by experts) was standardized by dividing it by the number of possibilities. Indeed, some drivers were allocated coefficients from 0 to 4 (5 possibilities) and others from 1 to 4 (4 possibilities). Afterwards, this standardized score was multiplied by the intra-domain weight and the inter-domain weight, as given by the expert. These results led to an overall weighted score for each driver and per expert: In this formula, OWSDri = overall weighted score for a specific driver; SDri = standardized score for a specific driver; WDri = intra-domain weight for a specific driver; WDoj = inter-domain weight for a specific driver included in a specific domain. Further-more, all drivers were ranked based on the median overall weighted score obtained for each driver and taking into account the answers of all the experts who answered the questionnaire. The statistical difference of the median, depending on the specific driver or the group of drivers considered, was assessed through a non-parametric Kruskal-Wallis equality-of-populations rank test (State SE 14.2; StataCorp, College Station, TX, USA).

Cluster Analysis
A cluster analysis was carried out using a regression tree analysis (Salford Predictive Modeler ® , Version 8.2, Salford Systems, San Diego, CA, USA). The median overall weighted score (median OWSDri) being a continuous variable, the aim was to obtain groups of drivers with minimal within-group variance, with comparable likelihood to play a role in the emergence of BB in cattle. In addition, the statistical difference between medians after grouping drivers in clusters was assessed using a non-parametric Kruskal-Wallis equalityof-populations rank test. Indeed, each driver was characterized by a median (based on all experts' answers), then drivers were grouped. The test allowed highlighting of potential significant differences between groups, in terms of driver medians, after clustering.

Sensitivity Analysis to Test the Robustness of the Expert Elicitation
In order to identify whether the ranking of BB drivers of emergence was influenced by the choice of experts, a sensitivity analysis was performed on the top five and top seven drivers. First, we started by ranking the drivers using the obtained median OWSDri. Second, an expert was excluded from the analysis and the ranking of the drivers was carried out using the same methodology as previously described. This was done expert by expert. Third, we counted the number of changes in the ranking, for each driver, only considering changes equal or more than one rank. These results were confirmed using a Kruskal-Wallis equality-of-populations rank test on experts.

Conclusions
Since scientific knowledge on drivers of emergence of BB in cattle is still incomplete and associated uncertainty is high, expert elicitation of knowledge and multi-criteria decision analysis, in addition with clustering and sensitivity analyses, allowed the identification of seven drivers of more importance on which to focus on future studies. The transport of live cattle asymptomatic carriers seems to be a key factor of introduction and spread of BB. Indeed, further quantitative import risk assessment and estimation of economic burden of BB are highly recommended. This expert elicitation of knowledge should be also refined in the coming years when more evidence data will be available. In this case, addition of an uncertainty index should be recommended during elicitation. The present methodology could be applied to other emerging animal diseases. The application of this methodology to a specific disease also allows highlighting or not the need for more investigations.

Data Availability Statement:
The data that support the findings of this study are available from the corresponding author upon request.

Acknowledgments:
The authors thank all experts who participated to this study. Their names can be found in Appendix A.

Conflicts of Interest:
The authors declare no conflict of interest.

Score 3
Disease was reported/present in the African continent

D2_2 European geographic proximity of the pathogen/disease to Europe
Score 0

Score 2
Disease has been reported in Europe in the past but is currently exotic.

Score 3
Disease is currently present in at least one European country which is NOT bordering your country

Score 4
Diseases is currently present in at least one of the countries bordering your country

Score 3
Low: Surveillance only in some EU member states (because they had cases of the disease) and only in some non-EU countries (not a disease reported in any international organisations)

D4_6
Proximity of livestock farm to wildlife and wildlife reservoirs of disease e.g., contact with wild or feral birds and animals which have been scavenging on landfill sites that contain contaminated animal products Score 0 Score 1 Negligible: Disease (re)emergence from wildlife and wildlife reservoir never reported The disease can be present in zoo animals, but it is not known to have been transmitted from zoo animals to livestock Score 2 Low: The disease can enter a zoo (e.g., with introduction of an infected exotic animal) but only accidental transmissions of the disease from zoo animals to livestock have been reported. Hence, zoos have a low effect on the (re)emergence of the disease in livestock of your country

Score 3 Moderate:
The disease can enter a zoo and be present in zoo animals but it needs a vector (biological/mechanical) for its transmission into livestock. Therefore, zoos have a moderate effect on the (re)emergence of the disease your country

Score 4
High: Disease can be introduced to a zoo via an infected imported animal, zoo animals can carry the disease that can easily jump to livestock animals Table A2. Cont.

D6_2
The rural(farm)-wildlife interface Score 0 Score 1 Negligible: the disease has never (re)emerged from the narrowing of the farm-wild interface Score 2 Low: the disease has a low probability to (re)emerge via the livestock farm-forest interface. The disease has been known to (re)emerge from the wild bush but very rarely Score 3 Moderate: the disease has a moderate probability of (re)emergence via the farm/wildlife interface. Barriers ( natural or artificial) are needed to keep the disease/pathogen (re)emerging in livestock

Score 4
High: there is a high probability for the disease to (re)emerge via the farm/forest interface. Barriers (natural or artificial) separating farms from natural forests are ineffective

Increase of autochthons (indigenous animal) wild mammals in Europe and neighbouring countries
Score 0 Not applicable: disease has not been reported in wildlife

Score 2
Low: there is a low probability of the disease (re)emerging and spreading through increased populations of endemic/migrating wild birds. Disease has spread from the endemic/migrating wild birds but only accidentally or under exceptional circumstances

Score 3
Moderate: there is a moderate probability of disease being introduced and spread through increased populations of endemic/migrating wild birds. They are hosts and in close contact with domestic livestock (i.e., poultry farms) may spread the disease

Score 4
High: there is a high probability for a disease to (re)emerge through increased populations of wild/migrating birds. These are hosts or reservoirs of the disease Table A2. Cont.

D6_5
Hunting Activities: hunted animals can be brought back to where livestock is present Score 0 Score 1 Negligible: The risk of the disease/pathogen of (re)emerging in livestock due to hunting activities is practically null Score 2 Low: disease is present in hunted wildlife and birds and only accidental cases have been reported in livestock that have (re)emerged because of hunting. The risk of the disease/pathogen of (re)emerging in livestock due to hunting activities is practically null

Score 3
Moderate: disease is present in hunted wildlife and birds but a certain control is established by the hunter

Score 4
High: disease is present in hunted wildlife and birds and hunting is one of the main modes of transmission of the disease to livestock

D6_6 Transboundary movements of terrestrial wildlife from other countries
Score 0 Not applicable: Disease is not carried by terrestrial wildlife Score 1 Negligible: (re)emergence of the disease by terrestrial movements of wildlife has only been suspected but never confirmed

Score 2
Low: There is a low probability for the disease to (re)emerge and spread through transboundary movements of terrestrial wildlife

Score 3
Moderate: There is a moderate probability for the disease to (re)emerge and spread through transboundary movements of terrestrial wildlife

D7_2 Human Immigration
Score 0 Score 1 Negligible: the immigration movements are a negligible driver of the disease (re)emergence in your country Score 2 Low: the immigration movements are a low driver of the disease (re)emergence in your country

Score 3
Moderate: the disease is currently present in countries where more immigrants come from and pathogen highly likely to enter through, clothes, shoes and or possession, but the current biosecurity measures in place are able to prevent the emergence of the disease in your country

Score 4
High: the immigration movement has a high effect as a driver on the emergence or re-emergence of disease in your country. Disease is highly likely to emerge using this route as biosecurity measures are not enough to avoid emergence of the disease

D7_5
Bioterrorism potential Score 0 Score 1 Negligible: the role of bioterrorism as a driver for a disease to (re)emerge is negligible: agent is available but difficult to handle or has a low potential of spread or generates few economic consequences Score 2 Low: the role of bioterrorism as a driver for a disease to (re)emerge is low: agent is available and easy to handle by professionals and labs but has a low spread

Score 3
Moderate: the role of bioterrorism as a driver for a disease to (re)emerge is moderate: agent available and easy to handle by professionals and labs and rapidly spreads

Score 4
High: the role of bioterrorism as a driver for a disease to (re)emerge is high: Agent is available and easy to handle by individuals and rapidly spreads D7_6 Inadvertent release of an exotic infectious agent from a containment facility e.g., Laboratory Score 0

Score 2
Low: the pathogen is present in a containment facility but its release is very unlikely as it is very easily contained

D8_2
Modification of the disease status (i.e., reportable disease becoming not reportable) or change in screening frequency due to a reduced national budget Score 0 Score 1 Negligible: modification of the disease status due to a reduced national budget has a negligible effect on the (re) emergence of the disease in your country Score 2 Low: modification of the disease status due to a reduced national budget has a low effect on the (re) emergence of the disease in your country

Score 3
Moderate: modification of the disease status due to a reduced national budget has a moderate effect on the (re) emergence of the disease in your country

Score 4
High: modification of the disease status due to a reduced national budget has a high effect on the (re) emergence of the disease in your country

D8_3
Decrease of resources allocated to the implementation of biosecurity measures at border controls (e.g., harbors or airports) Score 0 Score 1 Negligible: decreasing the resources allocated to the implementation of biosecurity measures has a negligible effect on the (re)emergence of the disease in your country. Disease has never been detected in the past in a harbor or airport Score 2 Low: decreasing the resources allocated to the implementation of biosecurity measures has a low effect on the (re)emergence of the disease in your country. The disease has been suspected to have entered other countries because of deficient biosecurity at border controls Score 3 Medium: decreasing the resources allocated to the implementation of biosecurity measures has a moderate effect on the (re)emergence of the disease in your country. The disease has been introduced in other countries because of deficient biosecurity at border controls

Score 4
High: decreasing the resources allocated to the implementation of biosecurity measures highly increases the risk of (re)emergence of the disease in your country. In the past, the disease has been introduced in other countries and in your country because of deficient biosecurity at border controls

D8_4
Most likely influence of (il)legal movements of live animals (livestock, pets, horses etc) from neighbouring/European Union member states (MS) for the disease to (re)emerge in your country

D8_5
Most likely influence of (il)legal movements of pets from Third countries for the disease to (re)emerge in Europe Score 0 Score 1 Negligible: increased (il)legal imports of animal subproducts such as skin, meat and edible products from EU member states have a negligible influence on the pathogen/disease (re)emergence in your country Score 2 Low: increased (il)legal imports of animal subproducts such as skin, meat and edible products from EU member states have a low influence on the pathogen/disease (re)emergence in your country

Score 3
Moderate: increased (il)legal imports of animal subproducts such as skin, meat and edible products from EU member states have a moderate influence on the pathogen/disease (re)emergence in your country

Score 4
High: increased (il)legal imports of animal subproducts such as skin, meat and edible products from EU member states have a high influence on the pathogen/disease (re)emergence in your country

D8_6
Most likely influence of increased (il)legal imports of non-animal products such as tires, wood, furniture from EU member states for the disease/pathogen to (re)emerge in your country Score 0 Score 1 Negligible: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a negligible influence on the pathogen/disease (re)emergence in your country

Score 2
Low: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a low influence on the pathogen/disease (re)emergence in your country

Score 3
Moderate: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a moderate influence on the pathogen/disease (re)emergence in your country

Score 4
High: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a high influence on the pathogen/disease (re)emergence in your country.

D8_7
Most likely influence of (il)legal movements of live animals (livestock, pets, horses etc) from Third countries for the disease to (re)emerge in your country Score 0 Score 1 Negligible: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a negligible influence on the pathogen/disease (re)emergence in your country Score 2 Low: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a low influence on the pathogen/disease (re)emergence in your country

Score 3
Moderate: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a moderate influence on the pathogen/disease (re)emergence in your country

Score 4
High: (il)legal movements of live animals (livestock, pets, horses etc) from Third countries have a high influence on the pathogen/disease (re)emergence in your country Table A2. Cont.

D8_8
Most likely influence of increased imports of animal sub-products such as skin, meat and edible products from Third countries, for the disease to (re)emerge in your country Score 0 Score 1 Negligible: Increased imports of animal subproducts such as skin, meat and edible products from Third countries have a negligible influence on the pathogen/disease (re)emergence in your country Score 2 Low: Increased imports of animal subproducts such as skin, meat and edible products from Third countries have a low influence on the pathogen/disease (re)emergence in your country

Score 3
Moderate: Increased imports of animal subproducts such as skin, meat and edible products from Third countries have a moderate influence on the pathogen/disease (re)emergence in your country

Score 4
High: Increased imports of animal subproducts such as skin, meat and edible products from Third countries have a high influence on the pathogen/disease (re)emergence in your country

D8_9
Most likely influence of increased (il)legal imports of non-animal products such as tires, wood, furniture from Third countries, for the disease to (re)emerge in your country Score 0 Score 1 Negligible: increased (il)legal imports of non-animal products such as tires, wood, furniture from Third countries have a negligible influence on the pathogen/disease (re)emergence in your country Score 2 Low: increased (il)legal imports of non-animal products such as tires, wood, furniture from Third countries have a low influence on the pathogen/disease (re)emergence in your country

Score 3
Moderate: increased (il)legal imports of non-animal products such as tires, wood, furniture from Third countries have a moderate influence on the pathogen/disease (re)emergence in your country Score 4 High: increased (il)legal imports of non-animal products such as tires, wood, furniture from Third countries have a high influence on the pathogen/disease (re)emergence in your country Number of drivers = 9, hence 90 points to be distributed within this domain for the intra-domain weighing Please give a score according to what you estimate is the importance of each driver in the (re)-emergence of specific disease(s). After the scoring, please balance each driver for each category of drivers. Balancing the criteria will rely on the distribution of points between the different proposed criteria under each category. The total number of points to be distributed among the drivers is specified for each category (each spreadsheet). e.g., category pathogen characteristics total 90 points; distance of outbreaks (spatial-temporal scales) a total of 30 points to be distributed.

2)
Intra-category weighing: The last step of the process will consist in the distribution of 80 points between the 8 categories of criteria (Pathogen characteristics, distance of outbreaks, etc.). This is on the 9th spreadsheet. The distribution will depend on which is believed to be the strongest category of drivers.
As an expert in the field, your collaboration will help us a lot for the good course of the project.
Thank you in advance for your collaboration and for the time spent in filling the file before the Date.
For any question, not hesitate to contact claude.saegerman@uliege.be