During an epidemic, the emergence of "variants of concern" is shaped by evolutionary pressures such as vaccination and competition. The COVID-19 pandemic led to an explosion in collection of genetic data and surveillance of evolving strains, but this has also highlighted the gap between our capacity to collect this data and our capability to use it to make predictions. Many public health agencies now collect genetic data on diseases such as flu and measles, but this data is not being utilized due to the absence of tools and expertise.

Phylodynamics is the study of the processes that give rise to phylogenetic trees. This is the theme of the 2024 Queen’s Workshop on Mathematical Ecology (following previous workshops in 2022: “Modeling Epidemics” and 2019: “Modeling Structured Populations”). This is a rapidly growing field fueled by the growth of genomic surveillance data. This workshop will aim to bring together researchers in the working in the following areas:

1. Stochastic models of disease spread and evolution The speakers under this theme will be researchers on stochastic systems, particular continuous time Markov chains that can be used to describe epidemiological disease spread in conjunction with genetic sampling of the infected population. The goal is to model the process of generating time series data on reported numbers of the diseases as well as the sub-sampled phylogenetic trees. Analysis of these models and formulations of the probability distribution of the trees can lead to interesting new mathematics that apply to a wide range of problems beyond those in epidemiology.

2. Perspectives from public health It is important that mathematical models of population dynamics be grounded in the practice of medicine and public health. The speakers for this session will be public health researchers who will provide this frame of reference. The goal is gain insight over the use of phenomenological versus mechanistic, deterministic versus stochastic models, and determine the best methods for applications of phylodynamic modeling.

3. Computational phylogenetics and statistical inference Modern statistical and computational tools, utilizing high-performance computing technology, allow us to simulate and fit more complex models than before. Simulation-based methods allow for efficient and full-information techniques to fit high-dimensional models, including those involving phylogenies, to data sets. The goal is to determine computational avenues for fitting both time series and genetic data, in collaboration with researchers from both the theoretical and applications perspective.

This workshop will make contributions to mathematical phylodynamics by (1) bringing together experts from different aspects of phylodynamic modeling so that we may all learn from each other, (2) to encourage interdisciplinary collaborations, and (3) to expose students and postdoctoral fellows to the different facets of mathematical epidemiology, so that that they may gain a wider understanding of the state-of-the-art applications of mathematics. We also hope that this workshop will encourage the organization of future events like this.