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

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 framework to simulated data and assess how the clustering algorithms captured the simulated clusters. We then apply this approach to data from a cohort study in Lima, Peru and examine the homogeneity of the clusters using a binary entropy measure. Results: We find cluster performance to be consistent across all edge trimming scenarios and clustering methods. We find high levels of entropy for age, sex, socioeconomic status, and individuals who work outside the house and use public transit, indicating these variables are heterogenous across clusters. Conclusions: We demonstrate approaches to analyze estimated directed pairwise transmission probabilities with network techniques. The approach is consistent across network construction and clustering methods. This method can be applied to any disease outbreak to understand its dynamics.