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

Disease incursion and transmission modelling can play an important role in elucidating important pathways anddynamics of transboundary diseases. It is an important pre-requisite for preparedness and rapid response. Amodel framework has been developed which makes use of global datasets to predict the probability of entry ofexotic animal pathogens to European Union (EU) member states (MSs) via some of the most likely routes ofintroduction: 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 awide range of pathogens, many of which have limited data. We demonstrate its application through four casestudy 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 agiven route differed across MSs whilst different pathogens were predicted as having the highest probability ofentry for the same route across MSs. Scenario analyses suggested that the probability of entry was heavilyinfluenced by the pathogen prevalence in the country of origin and the extent to which EU MSs pose a risk toeach other; the greatest risk was predominantly from countries within the EU. While we believe the input dataare obtained from high quality sources, there are still big issues with regards uncertainty in some areas, inparticular with regards to prevalence of pathogens in vector populations and consistency of reporting of pa-thogen prevalence in animals across all countries of the world. Thus, it is inevitable that there is a high degree ofuncertainty associated with the absolute values. However, the main strength of the model is the broad range ofanalyses over pathogens, EU MSs and routes of entry. The model is also relatively easy to update with new dataand 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 qualitativeearly warning systems, helping to drive risk-based surveillance activities, by providing detailed quantitativecomparisons to indicate which pathogens are most likely to enter the EU, by which route and into which areaswithin Europe.