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

Abstract

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.

Keywords: Besnoitia besnoiti; bovine besnoitiosis; cattle; clustering analysis; drivers; expert elicitation; multi-criteria decision analysis (MCDA); sensitivity analysis.

Figure 1

Boxplot of the relative importance of the eight domains of bovine besnoitiosis in cattle (N = 11 experts). 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.

Figure 2

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.

Figure 3

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).