Network Analysis of Pairwise Relative Tuberculosis Transmission Probabilities in Lima, Peru.

ABSTRACT:

Background

Identifying transmission events is important in understanding infectious disease dynamics. Such events are typically unobservable, particularly in diseases with long serial intervals such as tuberculosis (TB). We apply network techniques to identify transmission clusters and features shared within clusters.

Methods

We estimate directed pairwise transmission probabilities via an existing iterative algorithm that employs a modified Naïve Bayes classifier to incorporate demographic, clinical, and genetic data and use these probabilities to create a network. We explore noise reduction techniques to trim low probability edges. We apply clustering algorithms to group together individuals with TB based on edges informed by transmission probabilities. We apply our framewo

SUBMITTER: Shapiro AN 

PROVIDER: S-EPMC12668085 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature