Online phylogenetics using parsimony produces slightly better trees and is dramatically more efficient for large SARS-CoV-2 phylogenies than de novo and maximum-likelihood approaches

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 datasets do not fit this mould. There are currently over 5 million sequenced SARS-CoV-2 genomes in public databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between Likelihood and Parsimony approaches to phylogenetic inference. Maximum Likelihood (ML) methods are more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare. Therefore, it may be that approaches based on Maximum Parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger datasets. Here, we evaluate the performance of de novo and online phylogenetic approaches, and ML and MP frameworks, for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimizations produce more accurate SARS-CoV-2 phylogenies than do ML optimizations. Since MP is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo, we therefore propose that, in the context of comprehensive genomic epidemiology of SARS-CoV-2, MP online phylogenetics approaches should be favored.