Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting

Area of Science:

• Epidemiology
• Genomic Surveillance
• Infectious Disease Dynamics

Background:

• Tuberculosis (TB) remains a significant global health challenge, necessitating effective outbreak detection strategies.
• Previous work demonstrated the utility of genotype surveillance for predicting outbreaks in incident TB clusters.
• Endemic TB clusters present unique challenges for outbreak detection due to established transmission patterns.

Purpose of the Study:

• To propose and evaluate a novel method for detecting potential outbreaks within endemic tuberculosis clusters.
• To assess the performance of this method using real-world data.

Main Methods:

• Development of a detection method leveraging genotype surveillance data.
• Application of the method to identify possible outbreaks in endemic TB clusters.
• Validation of detected outbreaks using epidemiologic data and whole-genome sequencing (WGS).

Main Results:

• The proposed method identified 15 potential outbreaks among endemic TB clusters.
• Ten of these potential outbreaks had supporting epidemiologic data or WGS results available.
• Eight of the potential outbreaks were corroborated, indicating the method's efficacy.

Conclusions:

• Genotype surveillance provides a valuable tool for detecting potential tuberculosis outbreaks in endemic settings.
• The developed method demonstrates promise for enhancing TB surveillance and control efforts.
• Further validation and implementation of this approach could improve timely outbreak response.