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arXiv · 2405.17032

Exact phylodynamic likelihood via structured Markov genealogy processes

Aaron A. King·Qianying Lin·Edward L. Ionides
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Abstract

We show that each member of a broad class of Markovian population models induces a unique stochastic process on the space of genealogies. We construct this genealogy process and derive exact expressions for the likelihood of an observed genealogy in terms of a filter equation, the structure of which is completely determined by the population model. We show that existing phylodynamic methods based on the coalescent and linear birth-death processes are special cases. We derive some properties of filter equations and describe a class of algorithms that can be used to numerically solve them. Importantly, because these algorithms rely only on simulation of the population model, they retain the plug-and-play property upon which simulation-based inference depends. Our results open the door to statistically efficient likelihood-based phylodynamic inference for a much wider class of models than has been possible.

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Aaron A. King, Qianying Lin, Edward L. Ionides. 2024-05-27. Exact phylodynamic likelihood via structured Markov genealogy processes. https://doi.org/10.1016/j.tpb.2026.07.002

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