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

Area of Science:

• Virology
• Computational Biology
• Epidemiology

Background:

• Acute viral infections present major public health concerns.
• Viral evolution, immune escape, and disease severity are influenced by host-to-host transmission dynamics.
• Transmission bottlenecks, which reduce viral genetic diversity during spread, are critical for understanding disease emergence and evolution.

Purpose of the Study:

• To introduce ViralBottleneck, an R package for estimating viral transmission bottleneck size.
• To integrate six established methods for bottleneck size estimation within a single package.
• To provide a user-friendly tool for researchers studying viral transmission.

Main Methods:

• The ViralBottleneck R package integrates six methods: presence-absence, Kullback-Leibler (KL), binomial, two beta-binomial versions, and Wright-Fisher.
• Simulated datasets generated using SANTA-Sim were employed to test package functionality under various scenarios with known bottleneck sizes.
• The performance and output of each method were evaluated using these simulated datasets.

Main Results:

• The study demonstrated the functionality of the ViralBottleneck package using simulated data.
• Significant variations in transmission bottleneck size estimates were observed across the six integrated methods.
• The choice of estimation method demonstrably impacts the resulting bottleneck size estimations.

Conclusions:

• The ViralBottleneck package offers a comprehensive suite of tools for transmission bottleneck analysis.
• Methodological choices significantly influence the estimation of viral transmission bottleneck size.
• Accurate estimation of bottleneck size is crucial for predicting disease dynamics and informing public health interventions.