A spatial risk assessment model framework for incursion of exotic animal disease into the European Union Member States

• Published in: Microbial risk analysis Vol. 13; p. 100075
• Main Authors: Simons, Robin R.L., Horigan, Verity, Ip, Sophie, Taylor, Rachel A., Crescio, Maria I., Maurella, Cristiana, Mastrantonio, Gianluca, Bertolini, Silvia, Ru, Giuseppe, Cook, Charlotte, Adkin, Amie
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2019
• Subjects: European Union; Exotic disease; Incursion; Release assessment; Spatial model; Incursion; Spatial model; Exotic disease; European Union; Release assessment
• ISSN: 2352-3522; 2352-3530
• DOI: 10.1016/j.mran.2019.05.001

•Developed a quantitative model to assess the probability of entry of animal pathogens into the EU.•Routes of entry include live animals, trade of meat products and wild animal dispersion.•Utilises freely available global datasets on disease prevalence and trade.•Results compare African swine fever, Classical swine fever, Bluetongue and classical rabies.•Early warning system to highlight increase in probability due to changing trade or new outbreaks.
Disease incursion and transmission modelling can play an important role in elucidating important pathways and dynamics of transboundary diseases. It is an important pre-requisite for preparedness and rapid response. A model framework has been developed which makes use of global datasets to predict the probability of entry of exotic animal pathogens to European Union (EU) member states (MSs) via some of the most likely routes of introduction: legal trade of livestock and meat products, illegal trade of red meat, wild animal dispersion, windborne vector dispersion and human introduction of pets. The model was designed to be applicable for a wide range of pathogens, many of which have limited data. We demonstrate its application through four case study pathogens: African swine fever, Classical swine fever, Bluetongue and classical rabies.
The model results highlight the differences in probability between EU MSs; the absolute values for entry via a given route differed across MSs whilst different pathogens were predicted as having the highest probability of entry for the same route across MSs. Scenario analyses suggested that the probability of entry was heavily influenced by the pathogen prevalence in the country of origin and the extent to which EU MSs pose a risk to each other; the greatest risk was predominantly from countries within the EU. While we believe the input data are obtained from high quality sources, there are still big issues with regards uncertainty in some areas, in particular with regards to prevalence of pathogens in vector populations and consistency of reporting of pathogen prevalence in animals across all countries of the world. Thus, it is inevitable that there is a high degree of uncertainty associated with the absolute values. However, the main strength of the model is the broad range of analyses over pathogens, EU MSs and routes of entry. The model is also relatively easy to update with new data and a web based visualisation tool has been developed which allows users to interrogate the results of the model. As such, we believe that the model proposed here can be a useful quantitative complement to current qualitative early warning systems, helping to drive risk-based surveillance activities, by providing detailed quantitative comparisons to indicate which pathogens are most likely to enter the EU, by which route and into which areas within Europe.