The EpiQuant Framework for Computing Epidemiological Concordance of Microbial Subtyping Data.

Area of Science:

• Microbiology
• Epidemiology
• Bioinformatics

Background:

• Molecular subtyping is crucial for public health investigations of infectious diseases.
• A key assumption is that related isolates share epidemiological commonalities.
• Currently, no systematic method exists to evaluate the epidemiological basis of subtyping results.

Purpose of the Study:

• To develop a method for quantifying bacterial isolate similarity using sampling metadata.
• To create a framework for computing the epidemiological concordance of microbial typing results.
• To objectively assess the performance of different microbial subtyping methods.

Main Methods:

• Developed an analytical model to summarize bacterial isolate similarity using basic sampling parameters.
• Created the EpiQuant framework in R for statistical computing.
• Applied EpiQuant to 654 *Campylobacter jejuni* isolates from Canadian surveillance data.

Main Results:

• EpiQuant quantifies bacterial isolate similarity based on readily available sample metadata.
• The framework was used to examine the epidemiological concordance of clusters from two leading *C. jejuni* subtyping methods.
• Demonstrated the ability to assess the alignment between microbial epidemiological and molecular data.

Conclusions:

• EpiQuant provides a direct measure of bacterial isolate similarity using basic metadata.
• This facilitates objective assessment of subtyping method performance.
• Enables improved application of molecular subtyping in infectious disease investigations.