The role of parameterization in comparing source attribution models based on microbial subtyping for salmonellosis

Abstract

Source attribution methods attribute human cases of a zoonotic disease to a certain source putatively responsible for this disease. Identifying and quantifying the contribution of different sources to human infections is important for taking appropriate actions for reducing the exposure of the consumer to zoonotic pathogens. One widely used method is the microbial subtyping approach, whose principle is to compare the frequency of pathogen subtypes from different sources (e.g. animals or food) with the frequency of these subtypes in human cases. This paper studies the relationship between a Bayesian microbial subtyping approach described by Hald and coworkers subsequently modified by David and coworkers, here called the Hald model, and a frequentist approach known as the “Dutch model.” The comparison between the Bayesian and frequentist model is done for two data sets on salmonellosis in Germany from different time periods (year 2004–2007 and 2010–2011). The results of both approaches are in good agreement with each other for the used data. It is shown here mathematically that a certain parameterization can be used to transform the probabilistic Hald model into a deterministic form, which is equivalent to the Dutch model. That certain parameterization secures independence of the model outcomes from the choice of so‐called unique subtypes (which are unique in the sense that they are found exclusively in one of the sources). It is shown that deviating from that certain parameterization leads variations in the model outcome dependent on which unique subtypes are chosen in the process of modelling.

Comparison of approaches for source attribution of ESBL-producing Escherichia coli in Germany

“…In 2019, Jabin et al [14] used two different source attribution models to predict the origin of Salmonella amongst humans from different food sources (broilers, laying hens, pigs and turkeys) in Germany. Nevertheless, there was still lacking a similar study in Germany regarding ESBL-producing E. coli.…”

Building an International One Health Strain Level Database to Characterise the Epidemiology of AMR Threats: ESBL—AmpC Producing E. coli as An Example—Challenges and Perspectives

“…There are different approaches to source attribution and one of the most popular is to use microbial subtyping data. Microbial subtyping techniques can include serotyping, phage typing, AMR profiles, or molecular techniques, such as WGS [ 72 ]. With this available information (serotypes, resistance types, or genotypes), source attribution models can be successfully generated.…”