08.02.2017PhD - Assessment of the freedom from animal disease

There are 2 exciting PhD positions available on output based methods for the assessment of the freedom of animal disease :

* One at the University of Utrecht, the Netherlands (see below or here for details)

* One at Oniris in Nantes, France (see below or here for details)

Several European countries have implemented national or regional surveillance, control, or eradication programmes for endemic infections of cattle with little or no regulation at EU level (non-regulated endemic diseases). Such programmes bring tangible benefits to participating farmers and national economies, and are to be strongly supported. However, they also create difficulties for intra-community trade, as free trade has the potential to allow the movement of infectious agents into regions free from the infection.

The STOC-FREE project was granted by the European Food Safety Authority (EFSA) and local public bodies with the scope to develop and validate a new framework (STOC-FREE: Surveillance Tool for Outcome-based Comparison of FREEdom from infection) that enables a transparent and standardised comparison of confidence of freedom for control programs of both non-regulated and regulated diseases in the EU. Initially, Bovine viral diarrhoea virus (BVDV) will be used as an example disease. The international project is coordinated by Utrecht University and involves 8 partners from 6 countries (NL, FR, DE, IE, SE, UK). In this project, two PhD positions are offered, one at the Veterinary Faculty of Utrecht University in the Netherlands and one at Oniris, in Nantes, France. The work of the PhD students will be complementary and close collaboration is foreseen. To facilitate this collaboration a good knowledge of the English language, strong interpersonal skills and the willingness to work abroad are essential skills for the successful candidates.

The PhD student at Utrecht University will be responsible for the collection and analyses of data about control programs for BVDV in Europe. The PhD student at Oniris will develop the analytical methodology for an output-based, quantitative comparison of control programs. In an iterative process, the methodology will be applied by the PhD students on data from different BVDV control programs, leading to adaptation and final approval.

The PhD project at Utrecht University will have the following aims: 1) Develop a structured and generic protocol to describe control programmes for cattle diseases in the EU; 2) Apply the framework to determine the confidence of freedom of BVDV on animal, herd, sector and regional/national level in the Netherlands and other EU MS, 3) Evaluate the suitability of the developed framework to enable evaluation and optimisation of existing control programmes and 4) Apply the developed framework to other diseases e.g. BHV-1, Mycobacterium bovis, or paratuberculosis and determine the possibilities to generalize the developed framework for output-based comparison to a larger range of cattle diseases.

For details visit the homepage.

The aim of the PhD research project at Oniris is to develop a method allowing the estimation of a confidence of freedom from infection (probability of absence of infection) and the associated uncertainty from heterogeneous data inputs. The true infection state is considered a hidden state. Confidence of freedom can be estimated at different levels: animal, herd, sector and regional/national levels. The method will be developed and implemented using BVDV as an application, with different and heterogeneous regional datasets. This will be achieved through the following steps:

• Development of a conceptual model representing the course and dynamics of BVDV infection at different levels. This step will serve as the basis for the development of the statistical models.
• Identification and evaluation of the data sources that will be used as inputs in the statistical models. This part of the work will be carried out in close collaboration with the PhD student based at Utrecht University.
• Development of statistical models to estimate the confidence of freedom from infection. The performance of several models of varying complexity will be compared. The scenario tree methodology and latent class models will be considered. It will be left to the student to propose and explore other methods if relevant.
• Adaptation of the method for implementation with different data inputs.

The PhD student will adapt the method for its implementation with data from other member states and work for that in close collaboration with a PhD student at Utrecht University.

For details on the position visit the homepage.