Instructors. Aaron King and Edward Ionides
Open to PhD and Masters students. Suitably prepared undergraduates may be admitted with instructor permission.
Thu 1:00-2:30, location to be decided.
Description. Statistics plays a critical role in scientific investigation, enabling rigorous confrontation between hypothesis and data. Hypotheses may take the form of stochastic mechanistic models, and bringing such models into contact with data in a statistically efficient manner remains a cutting-edge challenge. New data types add to the challenge. In particular, biologists now commonly seek to extract information on disease transmission from virus genomes sampled from infected individuals: this is the subject of phylodynamics. In this course, we will examine existing phylodynamic inference methods and identify research opportunities for contributions to phylodynamic methodology. To this end, we will study the necessary background on inference for stochastic processes, evolutionary biology and epidemiology.
Course structure. The classroom is flipped: each week, we study a paper in class with an expectation (from class #2 onward) that everyone comes to class having read the paper and ready to discuss it, equipped with at least one question for the group to discuss. The last weeks of the class will move on to a group data analysis project, using the data, models, and methods covered in class.
Grading. This is a 1.5 credit course, meeting once a week throughout the semester. Grades will be based on attendance and participation.
Syllabus. The following schedule may be updated as the course proceeds.
[Presented as introduction in the first class] Grenfell, B. T., Pybus, O. G., Gog, J. R., Wood, J. L., Daly, J. M., Mumford, J. A., & Holmes, E. C. (2004). Unifying the epidemiological and evolutionary dynamics of pathogens. Science, 303(5656), 327-332. https://doi.org/10.1126/science.1090727
Kingman, J. F. C. (1982). The coalescent. Stochastic Processes and their Applications, 13(3), 235-248. https://doi.org/10.1016/0304-4149(82)90011-4
Volz, E. M., Kosakovsky Pond, S. L., Ward, M. J., Leigh Brown, A. J., & Frost, S. D. (2009). Phylodynamics of infectious disease epidemics. Genetics, 183(4), 1421-1430. https://doi.org/10.1534/genetics.109.106021
Stadler, T. (2010). Sampling-through-time in birth–death trees. Journal of Theoretical Biology, 267(3), 396-404. https://doi.org/10.1016/j.jtbi.2010.09.010
King, A. A., Lin, Q., & Ionides, E. L. (2022). Markov genealogy processes. Theoretical Population Biology, 143, 77-91. https://doi.org/10.1016/j.tpb.2021.11.003
Volz, E. M., Koelle, K., & Bedford, T. (2013). Viral phylodynamics. PLOS Computational Biology, 9(3), e1002947. https://doi.org/10.1371/journal.pcbi.1002947
Volz, E. M. (2012). Complex population dynamics and the coalescent under neutrality. Genetics, 190(1), 187-201. https://doi.org/10.1534/genetics.111.134627
Vaughan, T. G., & Stadler, T. (2025). Bayesian Phylodynamic Inference of Multitype Population Trajectories Using Genomic Data. Molecular Biology and Evolution, 42(6), msaf130. https://doi.org/10.1093/molbev/msaf130
King, A. A., Lin, Q., & Ionides, E. L. (2025). Exact phylodynamic likelihood via structured Markov genealogy processes. _ArXiv:2405.17032. https://doi.org/10.48550/arXiv.2405.17032
Ki, C., & Terhorst, J. (2022). Variational phylodynamic inference using pandemic-scale data. Molecular Biology and Evolution, 39(8), msac154. https://doi.org/10.1093/molbev/msac154
11-14. Team practicum: one or more groups identify a research project and make several weeks of progress.