Abstract
ABSTRACTA 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.
Highlights
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
The epidemiological cluster cohesion (ECC) distributions show that favoring temporal interactions results in a significantly lower average ECC value (0.458 Ϯ 0.173) than those calculated with a greater emphasis on source relationships (0.640 Ϯ 0.124; P Ͻ 0.001) or when the “balanced” coefficient set was used (0.564 Ϯ 0.141; P ϭ 0.003)
By subjecting our data set of 654 C. jejuni isolates to both multilocus sequence typing (MLST) and comparative genomic fingerprinting (CGF) and comparing the ECC values of clusters generated by subtyping methods with increasing resolution (i.e., clonal complex (CC) Ͻ sequence type (ST) Ͻ CGF), we aimed to test the hypothesis that strain typing methods with higher resolution would separate isolates of C. jejuni into clusters demonstrating higher epidemiological concordance
Summary
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. Clusters of genetically or phenotypically related isolates are produced by using one or more molecular subtyping methods, and relevant epidemiological attributes, such as membership in an outbreak group, are superimposed and subjected to interpretation on a cluster-by-cluster basis, with additional context such as subtype reproducibility, subtype prevalence, and subtype variability in the organism being considered [3,4,5] While this general approach represents a pragmatic solution to the need for interpretation criteria based on epidemiological relevance, it lacks the systematic rigor required to comprehensively assess subtyping results and their concordance with underlying characteristics related to the ecology and epidemiology of the bacterial isolates in question. We assess the utility of this framework on a data set of 654 isolates of the important zoonotic pathogen Campylobacter jejuni sampled from across Canada and demonstrate how the model can be used to (i) quantify the epidemiological similarity between C. jejuni isolates, (ii) assess the relative abilities of subtyping methods to cluster isolates into cohesive epidemiologically linked groups, and (iii) identify subtype clusters with significantly increased specificity for the underlying epidemiology of bacterial isolates, facilitating targeted epidemiological investigations
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