Maximum likelihood pandemic-scale phylogenetics.

Genomic data plays an essential role in the study of transmissible disease, as exemplified by its current use in identifying and tracking the spread of novel SARS-CoV-2 variants. However, with the increase in size of genomic epidemiological datasets, their phylogenetic analyses become increasingly impractical due to high computational demand. In particular, while maximum likelihood methods are go-to tools for phylogenetic inference, the scale of datasets from the ongoing pandemic has made apparent the urgent need for more computationally efficient approaches. Here we propose a new likelihood-based phylogenetic framework that greatly reduces both the memory and time demand of popular maximum likelihood approaches when analysing many closely related genomes, as in the scenario of SARS-CoV-2 genome data and more generally throughout genomic epidemiology. To achieve this, we rewrite the classical Felsenstein pruning algorithm so that we can infer phylogenetic trees on at least 10 times larger datasets with higher accuracy than existing maximum likelihood methods. Our algorithms provide a powerful framework for maximum-likelihood genomic epidemiology and could facilitate similarly groundbreaking applications in Bayesian phylogenomic analyses as well.