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Summary Background Among the most consequential unknowns of the devastating COVID-19 pandemic are the durability of immunity and time to likely reinfection. There are limited direct data on SARS-CoV-2 long-term immune responses and reinfection. The aim of this study is to use data on the durability of immunity among evolutionarily close coronavirus relatives of SARS-CoV-2 to estimate times to reinfection by a comparative evolutionary analysis of related viruses SARS-CoV, MERS-CoV, human coronavirus (HCoV)-229E, HCoV-OC43, and HCoV-NL63. Methods We conducted phylogenetic analyses of the S, M, and ORF1b genes to reconstruct a maximum-likelihood molecular phylogeny of human-infecting coronaviruses. This phylogeny enabled comparative analyses of peak-normalised nucleocapsid protein, spike protein, and whole-virus lysate IgG antibody optical density levels, in conjunction with reinfection data on endemic human-infecting coronaviruses. We performed ancestral and descendent states analyses to estimate the expected declines in antibody levels over time, the probabilities of reinfection based on antibody level, and the anticipated times to reinfection after recovery under conditions of endemic transmission for SARS-CoV-2, as well as the other human-infecting coronaviruses. Findings We obtained antibody optical density data for six human-infecting coronaviruses, extending from 128 days to 28 years after infection between 1984 and 2020. These data provided a means to estimate profiles of the typical antibody decline and probabilities of reinfection over time under endemic conditions. Reinfection by SARS-CoV-2 under endemic conditions would likely occur between 3 months and 5·1 years after peak antibody response, with a median of 16 months. This protection is less than half the duration revealed for the endemic coronaviruses circulating among humans (5–95% quantiles 15 months to 10 years for HCoV-OC43, 31 months to 12 years for HCoV-NL63, and 16 months to 12 years for HCoV-229E). For SARS-CoV, the 5–95% quantiles were 4 months to 6 years, whereas the 95% quantiles for MERS-CoV were inconsistent by dataset. Interpretation The timeframe for reinfection is fundamental to numerous aspects of public health decision making. As the COVID-19 pandemic continues, reinfection is likely to become increasingly common. Maintaining public health measures that curb transmission—including among individuals who were previously infected with SARS-CoV-2—coupled with persistent efforts to accelerate vaccination worldwide is critical to the prevention of COVID-19 morbidity and mortality. Funding US National Science Foundation.

Since its original inception over 150 years ago by Darwin, we have made tremendous progress toward the reconstruction of the Tree of Life. In particular, the transition from analyzing datasets comprised of small numbers of loci to those comprised of hundreds of loci, if not entire genomes, has aided in resolving some of the most vexing of evolutionary problems while giving us a new perspective on biodiversity. Correspondingly, phylogenetic trees have taken a central role in fields that span ecology, conservation, and medicine. However, the rise of big data has also presented phylogenomicists with a new set of challenges to experimental design, quantitative analyses, and computation. The sequencing of a number of very first genomes presented significant challenges to phylogenetic inference, leading fungal phylogenomicists to begin addressing pitfalls and postulating solutions to the issues that arise from genome-scale analyses relevant to any lineage across the Tree of Life. Here we highlight insights from fungal phylogenomics for topics including systematics and species delimitation, ecological and phenotypic diversification, and biogeography while providing an overview of progress made on the reconstruction of the fungal Tree of Life. Finally, we provide a review of considerations to phylogenomic experimental design for robust tree inference. We hope that this special issue of Advances in Genetics not only excites the continued progress of fungal evolutionary biology but also motivates the interdisciplinary development of new theory and methods designed to maximize the power of genomic scale data in phylogenetic analyses.