TiTUS: Sampling and Summarizing Transmission Trees with Multi-strain Infections

Abstract

Motivation:The combination of genomic and epidemiological data hold the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. Results: We formulate the Direct Transmission Inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce TiTUS, a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritizes parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS's ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. Availability: https://github.com/elkebir-group/TiTUS