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transNetwork

R-CMD-check The goal of transNetwork is to provide a unified framework for simulating, reconstructing, and visualizing infectious disease transmission networks using genomic and epidemiological data.

Features

  • Simulation: Generate stochastic outbreaks with custom latent and infectious periods.
  • Reconstruction: Summarize MCMC outputs from a Julia program Bayesian Estimation of Transmission Networks (BetNet).
  • Phylogenetics: Convert transmission histories into phylogenetic trees.
  • Metrics: Calculate pairwise transmission distances and Most Recent Common Infectors (MRCI).
  • Visualization: High-level plotting functions for networks, timelines, and trees.

Installation

You can install the development version of transNetwork from GitHub with:

# install.packages("pak")
pak::pak("lliu1871/transNetwork")

Note: This will automatically install the necessary dependencies, including ape, igraph, and phybase.

Quick Start

Here is a basic example of how to simulate an outbreak and visualize the transmission network:

library(transNetwork)
library(TransPhylo)

# 1. Simulate an outbreak of 50 individuals
outbreak_data <- simulate_outbreak(target_size = 50, infection_rate = 1.5)

# 2. Build a time tree from the transmission distances
time_tree <- build_timetree(outbreak_data, plot = TRUE)

# 3. TransPhylo analysis
ptree <- ptreeFromPhylo(time_tree, dateLastSample = 2007.964)
res <- inferTTree(ptree, mcmcIterations = 10000, w.shape = 10, w.scale = 0.1, dateT = 2008)

# 4. Plot the estimated transmission network
matrixwiw <- computeMatWIW(res, burnin = 0.5)
netedges <- transmission_edges_matrixwiw(matrixwiw)
transplot(netedges, style = 1)

Research Context

This package was developed for academic researchers in evolutionary genomics and phylogenetics. It specifically addresses the “coalescent lag” between transmission events and genetic divergence, allowing for more accurate benchmarking of molecular clock models in outbreak settings.