ProfessorErik Volz

I study the interaction of epidemiological dynamics and the evolution of pathogens, including

* Development of population genetic theory for how epidemiology shapes microbial evolution
* Statistical inference methodology for epidemiological parameters from genetic data
* Phylogenetic & phylodynamic methodology for handling pathogen genetic data.

Applications of this work are focused on the surveillance of infectious diseases, including outbreak detection, outbreak investigation, and epidemic forecasting. I also develop statistical methodology for fitting epidemic models to genomic data and study mathematical models for infectious disease dynamics, particularly network models.

I am an active developer of scientific software for infectious disease modeling and analysis, including:

PhyDyn: A BEAST2 package for Bayesian phylogenetics with epidemic and demographic models.
treedater: An R package for fast and scalable dating of phylogenetic trees and estimating molecular clock rates.
mlesky: A tool for non-parametric inference of effective population size through time.
treestructure: An R package for detecting population structure in phylogenetic trees and identifying growing outbreaks.
Coalescent.jl: A Julia package for coalescent modeling and simulation in phylogenetics.


I have contributed to a variety public health initiatives, including the UK COVID-19 Genomics Consortium, the World Health Organization’s efforts on SARS-CoV-2 and mpox, and the development of national pathogen surveillance systems. My work aims to bridge the gap between pathogen genomics and actionable public health outcomes.