Methods of quantifying and visualising outbreaks of tuberculosis using genotypic information

Area of Science:

• Molecular Epidemiology
• Infectious Disease Transmission Dynamics
• Bioinformatics and Computational Biology

Background:

• Genotypic data from pathogenic isolates are crucial for understanding infectious disease spread.
• Current molecular epidemiological studies often employ phylogenetic reconstruction and clustering indices.
• Tuberculosis (TB) is a significant global health concern, necessitating accurate transmission assessment.

Purpose of the Study:

• To critically evaluate existing methods for analyzing genotypic data in molecular epidemiological studies of tuberculosis.
• To highlight the inaccuracies in outbreak inference due to the failure to account for marker mutation rates.
• To propose novel visual representations of genotypic data, including graphs and trees, and discuss index modifications.

Main Methods:

• Critique of current phylogenetic and clustering index methods used in molecular epidemiology.
• Development of a new graphical and tree-based approach for visualizing genotypic data.
• Analysis of marker mutation rates and their impact on transmission inference.

Main Results:

• Current methods may lead to errors in outbreak inference by neglecting marker mutation rates.
• Proposed visual methods offer a more intuitive and potentially accurate representation of genotypic relationships.
• Modifications to existing indices can improve their utility in assessing transmission patterns.

Conclusions:

• Accurate molecular epidemiological studies require accounting for the mutation rates of genetic markers.
• Novel visualization techniques can enhance the interpretation of genotypic data for disease transmission studies.
• The discussed methods are broadly applicable to directly transmissible clonal pathogens beyond tuberculosis.