The EpiQuant Framework for Computing Epidemiological Concordance of Microbial Subtyping Data
| Autor: | Frank Pollari, James E. Thomas, Benjamin M. Hetman, Clifford G. Clark, Steven K. Mutschall, Eduardo N. Taboada, Victor P. J. Gannon |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Microbiology (medical) medicine.medical_specialty Canada Computer science Epidemiology Concordance 030106 microbiology Computational biology Bioinformatics Campylobacter jejuni Objective assessment 03 medical and health sciences molecular subtyping Campylobacter Infections medicine Humans epidemiological concordance Molecular Epidemiology Models Statistical Molecular epidemiology biology biology.organism_classification Microbial typing Subtyping Metadata Molecular Typing 030104 developmental biology ecological epidemiology whole-genome sequencing sampling metadata Genome Bacterial |
| Zdroj: | Journal of Clinical Microbiology |
| ISSN: | 1098-660X 0095-1137 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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