TNet

TNet infers disease transmission networks from phylogenetic data by integrating multiple strain sequences per host to account for within-host strain diversity.

Key Features:

• Phylogenetic approach: Uses phylogeny-based inference to map relationships among pathogen sequences for transmission analysis.
• Within-host strain diversity: Integrates multiple strain sequences from each sampled host to represent within-host diversity in the inference.
• Scalability: Applies methods intended to handle large datasets with diverse characteristics.
• Ambiguity resolution: Distinguishes between ambiguous and unambiguous transmission events in inferred networks.

Scientific Applications:

• Simulation benchmarking: Evaluated on 560 simulated transmission networks with varying sizes and characteristics.
• Empirical benchmarking: Tested on 10 real datasets with known transmission histories and compared against phyloscanner and SharpTNI.
• SARS-CoV-2 transmission mapping: Applied to a large dataset of SARS-CoV-2 genomes from multiple countries to map geographical transmission networks.

Methodology:

TNet employs a phylogenetic approach that integrates multiple strain sequences per sampled host to construct transmission networks and distinguishes ambiguous versus unambiguous transmission events.