Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could facilitate outbreak control efforts and highlight new variants that warrant further study. Here we develop an analytical epidemiological model that infers the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that strongly affect the transmission rate, both within and outside the Spike protein. We also quantify the effects of travel and competition between different lineages on the inferred transmission effects of mutations. Importantly, our model detects lineages with increased transmission as they arise. We infer significant transmission advantages for the Alpha and Delta variants within a week of their appearances in regional data, when their regional frequencies were only around 1%. Our model thus enables the rapid identification of variants and mutations that affect transmission from genomic surveillance data.