Phylogenetics has a crucial role in genomic epidemiology. Enabled by unparalleled volumes of genome sequence data generated to study and help contain the COVID-19 pandemic, phylogenetic analyses of SARS-CoV-2 genomes have shed light on the virus’s origins, spread, and the emergence and reproductive success of new variants. However, most phylogenetic approaches, including maximum likelihood and Bayesian methods, cannot scale to the size of the datasets from the current pandemic. We present ‘MAximum Parsimonious Likelihood Estimation’ (MAPLE), an approach for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. MAPLE infers SARS-CoV-2 phylogenies more accurately than existing maximum likelihood approaches while running up to thousands of times faster, and requiring at least 100 times less memory on large datasets. This extends the reach of genomic epidemiology, allowing the continued use of accurate phylogenetic, phylogeographic and phylodynamic analyses on datasets of millions of genomes.

系统发育学在基因组流行病学中起着至关重要的作用。借助为研究和帮助遏制 COVID-19 大流行而生成的大量基因组序列数据，SARS-CoV-2 基因组的系统发育分析揭示了病毒的起源、传播以及新变种的出现和繁殖成功。然而，大多数系统发育方法，包括最大似然法和贝叶斯方法，都无法扩展到当前大流行的数据集的大小。我们提出了“最大简约似然估计”(MAPLE)，这是一种以前所未有的规模对流行病学基因组数据集进行基于似然的系统发育分析的方法。MAPLE 比现有的最大似然法更准确地推断 SARS-CoV-2 系统发育，同时运行速度高达数千倍，并且在大型数据集上需要至少 100 倍的内存。这扩展了基因组流行病学的范围，允许继续对数百万基因组的数据集使用准确的系统发育、系统地理学和系统动力学分析。