Early prediction of Mycobacterium tuberculosis transmission clusters using supervised learning models.

Identifying individuals with tuberculosis (TB) with a high risk of onward transmission can guide disease prevention and public health strategies. Here, we train classification models to predict the first sampled isolates in Mycobacterium tuberculosis transmission clusters from demographic and disease data. We find that supervised learning, in particular balanced random forests, can be used to develop predictive models to identify people with TB that are more likely associated with TB cluster growth, with good model performance and AUCs of ≥ 0.75. We also identified the most important patient and disease characteristics in the best performing classification model, including host demographics, site of infection, TB lineage, and age at diagnosis. This framework can be used to develop predictive tools for the early assessment of potential cluster growth to prioritise individuals for enhanced follow-up with the aim of reducing transmission chains.
Competing Interests: Competing interests The authors declare no competing interests. Ethics declarations Ethics were obtained from the University of British Columbia (certificate H12-00910) and informed consent for participation in the study was not required, as determined by institutional REB review.
(© 2024. The Author(s).)