ViralBottleneck: an R package for estimating viral transmission bottlenecks from deep sequencing data using multiple methods.

Acute viral infections pose significant public health challenges. Since viral evolution, immune escape, and infection severity are influenced by how viruses spread between hosts, understanding transmission bottlenecks is crucial for predicting disease dynamics and developing effective control strategies. Transmission bottlenecks reduce viral population size and genetic diversity as the virus spreads to new hosts. Bottleneck size, defined as the number of viral individuals successfully establishing infection in a new host, varies across transmission events and can influence disease emergence and virus evolution. In this study, we introduce ViralBottleneck, an R package integrating six established methods for estimating transmission bottleneck size: the presence-absence method, Kullback-Leibler (KL) method, binomial method, two versions of the beta-binomial method, and the Wright-Fisher method. We demonstrate the package's functionality using simulated datasets generated with SANTA-Sim under different scenarios with known bottleneck sizes. Our results reveal considerable variation in estimates across methods, highlighting the impact of methodological choice on bottleneck size estimation. The code and associated tutorial are available at https://github.com/BowenArchaman/ViralBottleneck.