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 respiratory diseases 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 and use these probabilities to create a network. We explore noise reduction techniques to trim low probability edges. We group individuals with TB based on edges informed by transmission probabilities via network clustering algorithms. We apply our framework to simulated data and assess clustering algorithm performance. We then apply this approach to data from a cohort study in Lima, Peru and examine 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, implying heterogeneity, for age, sex, socioeconomic status, individuals who work outside the house, and people using public transit.

CONCLUSIONS: We analyze estimated directed pairwise transmission probabilities with network techniques. The approach is consistent across network construction and clustering methods and can be applied to any disease outbreak to understand its dynamics.