The aim of WP1, which is led by ONIRIS France, is to develop a method (STOC-FREE MODEL) for the quantitative comparison of the confidence in freedom of disease in different control programmes for non-regulated diseases in the EU.
The method STOC-FREE MODEL will allow the estimation of the confidence of freedom from infection and the associated uncertainty from heterogeneous data inputs available for different epidemiological units such as animal, herd, sector, region or country. The method will be developed and evaluated using BVDV in cattle as an example disease.
WP1: Highlights of year 1
During the first year, WP1 focussed on the development of a conceptual model representing the course and dynamics of infection at different levels and the exploration of possible statistical methods that showed potential to be used in this specific context. From 5 September on, PhD student Mathilde Mercat started to work on the project.
The conceptual model described the infection process for the STOC free case disease - BVD - at 3 levels. At the animal level, the different infection states and the transitions between states (such as susceptible, infectious or resistant) were evaluated. At the herd level, the model considered herd demography, contact structure and the transmission pathways. At the territory level, the model represented possible transmission pathways from outside to within the territory. The conceptual model was developed and mapped the different types of information that existed for a given infectious disease onto the true status regarding infection.
The model connected:
· The biological system: the true status regarding infection which is of interest for different levels of analysis: animal, herd and territory.
· Information that is extremely diverse. Conceptually, two types of information that are different in nature can be distinguished:
o Information generated and collected to specifically detect the pathogen of interest such as test results from control programmes
o Information associated with an increased probability of pathogen presence such as risk factors of infection
The conceptual model was delivered in April 2018 and will be used to design the appropriate statistical models that will integrate different pieces of information (data) for the estimation of probabilities of being in each single state of interest (outcome) at different levels.
After evaluating and discussing different statistical approaches, development of a Bayesian network model appeared the most promising method to use in STOC free. Bayesian networks are flexible and allow for heterogeneous input information. Such information can be incorporated by inclusion of
prior distributions for the parameters in the model. The prior distributions can be based on default information on for example country level but can be tailored to each specific situation by entering more specific information. Data to specify the distributions to specific situations can be obtained from databases of control programmes, demographic data and contact structures between herds that will have a heterogeneous nature. In addition, frequency of occurrence and risk estimates for factors that influence either the probability of introduction or delayed detection of the infection in an animal or herd will be included in the model.