Salmonella source attribution using molecular typing data

The Danish Salmonella source attribution model uses a microbial subtyping approach to attribute cases to their animal reservoirs, i.e., it compares the number of human cases caused by different Salmonella subtypes with the distribution of the same subtypes isolated from various food-animal sources, also taking into account the differences in consumption of the meats/eggs included in the model.



Molecular typing methods are increasingly used to subntype foodborne pathogens isolated through animal, food and public health surveillance. In particular, MLVA (Multiple-Locus Variable number tandem repeat Analysis) is routinely used to subtype Salmonella isolates in Denmark. These methods are expected to replace phenotypic methods in the near future.



Using molecular data for source attribution is challenging for several reasons, mostly related to a dificul balance between specificity of the method and the required discrimination power for source attribution.



The objectives of this project are to evaluate how MLVA-based data performs in different source attribution approaches, to identify the most appropriate method for routine Salmonella source attribution, and to assess the utility of its results for the decision-making process in Denmark.