Source attribution via microbial subtyping – A study towards a more practical approach
Source attribution methods attribute cases of foodborne disease to the food vehicle or other source responsible for illness. Identifying and quantifying the contribution of the different zoonotic sources to human infections is important for reducing the exposure of the consumer to zoonotic pathogens. One of these methods is the microbial subtyping approach, the principle of which is to compare the subtypes of isolates from different sources (e.g. animals, food) with the same subtypes isolated from humans. Following this approach we analysed the structure and mathematical characteristics of a Bayesian source attribution model described by Hald and colleagues (2004), and subsequently modified by David and colleagues (2012). This analysis led us to the proposition of a modifieddata-based source attribution framework, which avoids any convergence problems of the Bayesian approach by solving the model equations deterministically. The results are in good accordance with the results of the Bayesian framework and the model is able to cope with many different structured data sets. We analysed the impact of each data component on the model outcome and give insight into the requirements regarding the quality of data necessary for microbiologic source attribution. Additionally,the modelling set up allows for incorporating uncertainty in data via Monte Carlo simulation. The modelling work will be presented with two different data sets on Salmonella from two different time periods (2004-2007 and 2010). The identified and quantified contribution of each source is compared and discussed for the different years.