Decision support using advanced modelling concepts in Epidemiology : enhancing future bovine herpesvirus type 1 eradication in Ireland

Infectious Bovine Rhinotracheitis (IBR) is a respiratory viral disease of cattle. It is caused by the highly contagious Bovine Herpesvirus Type 1 (BoHV-1), which is prevalent in many member states of the European Union (EU). Infected animals are affected by a higher risk of mortality, lower milk production and loss of body weight, resulting in severe economic losses for the dairy and beef industry. To reduce the economic losses, some countries in the EU have implemented virus control programmes and have successfully eradicated the virus. Other member states are still suffering from the virus and have not yet taken measures against it. This includes Ireland, where recent cross-sectional studies estimate 70-90% of all herds being sero-positive for BoHV-1 antibodies. The threat of a ban on live cattle exports from Ireland to countries free from BoHV-1 or countries where an EU-approved control programme is being implemented would further intensify economic losses. Therefore, stakeholders interest in the development of an Irish BoHV-1 eradication programme is increasing. The main objective of this thesis is to support the decision making process in the planning a possible BoHV-1 eradication programme in Ireland. For this purpose, machine learning methods, as well as mechanistic epidemiological modelling are applied, intending to create a multi-layered understanding of the demographic and epidemiological processes relevant to BoHV-1 control. The findings presented in this work serve as a basis for policy recommendations on the management and planning of BoHV-1 control and testing measures and are sought to have direct influence on policy decisions in the animal health sector.