The EpiQuant Framework for Computing Epidemiological Concordance of Microbial Subtyping Data

Abstract

A fundamental assumption in the use and interpretation of microbial subtyping results for public health investigations is that isolates that appear to be related based on molecular subtyping data are expected to share commonalities with respect to their origin, history, and distribution. Critically, there is currently no approach for systematically assessing the underlying epidemiology of subtyping results. Our aim was to develop a method for directly quantifying the similarity between bacterial isolates using basic sampling metadata and to develop a framework for computing the epidemiological concordance of microbial typing results. We have developed an analytical model that summarizes the similarity of bacterial isolates using basic parameters typically provided in sampling records, using a novel framework (EpiQuant) developed in the R environment for statistical computing. We have applied the EpiQuant framework to a data set comprising 654 isolates of the enteric pathogen Campylobacter jejuni from Canadian surveillance data in order to examine the epidemiological concordance of clusters obtained by using two leading C. jejuni subtyping methods. The EpiQuant framework can be used to directly quantify the similarity of bacterial isolates based on basic sample metadata. These results can then be used to assess the concordance between microbial epidemiological and molecular data, facilitating the objective assessment of subtyping method performance and paving the way for the improved application of molecular subtyping data in investigations of infectious disease.

Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

“…For QMRA purposes, a range of thresholds capturing the uncertainty around the degree of relatedness between isolates can be used and integrated into a QMRA model using a combination of scenario-based and stochastic approaches. In the future, the use of WGS for outbreak investigation and its increasing use in surveillance of foodborne pathogens will likely improve our knowledge of the structure of bacterial populations, and the collection of isolates from large and representative samples of the global population into open-source databases will provide a scientific basis to define appropriate distance thresholds, i.e., combining good epidemiological concordance (i.e., ability to group epidemiologically related isolates) and discriminatory power (i.e., ability to distinguish non-epidemiologically related isolates) (Van Belkum et al, 2007; Hetman et al, 2017). The uncertainty around the degree of relatedness between isolates, and therefore around risk estimates, will decrease accordingly.…”

Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016

“…This can be quite labour intensive, especially if there are many identified clusters stretching across multiple regions. Only recently, a study has been published that introduced a framework for computing epidemiological concordance of microbial subtyping data of Campylobacter jejuni [19]. Epidemiological cluster cohesion is based on time, geographical location, and environmental source distances with adjustable weights.…”