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The emergence of the COVID-19 epidemic in the United States (U.S.) went largely undetected due to inadequate testing. New Orleans experienced one of the earliest and fastest accelerating outbreaks, coinciding with Mardi Gras. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large-scale events accelerate transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana had limited diversity compared to other U.S. states and that one introduction of SARS-CoV-2 led to almost all of the early transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras, and the festival dramatically accelerated transmission. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate epidemics.

Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution. DOI: http://dx.doi.org/10.7554/eLife.01914.001.

Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. Here, we introduce a coalescent-based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. This algorithm provides the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full-genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses. [ABSTRACT FROM AUTHOR]

The global-scale epidemiology and genome-wide evolutionary dynamics of influenza B remain poorly understood compared with influenza A viruses. We compiled a spatio-temporally comprehensive dataset of influenza B viruses, comprising over 2,500 genomes sampled worldwide between 1987 and 2015, including 382 newly-sequenced genomes that fill substantial gaps in previous molecular surveillance studies. Our contributed data increase the number of available influenza B virus genomes in Europe, Africa and Central Asia, improving the global context to study influenza B viruses. We reveal Yamagata-lineage diversity results from co-circulation of two antigenically-distinct groups that also segregate genetically across the entire genome, without evidence of intra-lineage reassortment. In contrast, Victoria-lineage diversity stems from geographic segregation of different genetic clades, with variability in the degree of geographic spread among clades. Differences between the lineages are reflected in their antigenic dynamics, as Yamagata-lineage viruses show alternating dominance between antigenic groups, while Victoria-lineage viruses show antigenic drift of a single lineage. Structural mapping of amino acid substitutions on trunk branches of influenza B gene phylogenies further supports these antigenic differences and highlights two potential mechanisms of adaptation for polymerase activity. Our study provides new insights into the epidemiological and molecular processes shaping influenza B virus evolution globally. [ABSTRACT FROM AUTHOR]

• Spatiotemporal analysis of dengue virus (DENV) 1 and 2 in Africa. • Minimum 10 and eight introductions into Africa of DENV-1 and cosmopolitan DENV-2. • Recent introductions are circulating but also a long-established African clade. • After introduction into Africa, a limited geographical spread can be observed. • Active genomic surveillance might limit sero- and genotype spread and disease. The origin and spread of dengue virus (DENV) circulating in Africa remain poorly characterized, with African sequences representing <1% of global sequence data. Whole genome sequencing was performed on serum samples (n = 29) from an undifferentiated fever study in 2016 in the Democratic Republic of Congo (DRC), and from febrile travelers returning from Africa. The evolutionary history of the newly acquired African DENV-1 (n = 1) and cosmopolitan genotype DENV-2 (n = 18) genomes was reconstructed using a phylogeographic, time-scaled Bayesian analysis on a curated DENV panel including all known African sequences. A minimum of 10 and eight introductions could be identified into Africa for DENV-1 and cosmopolitan DENV-2, respectively, almost all originating from Asia. Three introductions were previously unknown. The currently circulating virus comprises mainly the recently introduced clades and one long-established African clade. Robust geographical clustering suggests limited spread of DENV after each introduction. Our data identified the DRC as the source of the 2018 Angolan DENV-2 epidemic, and similarly, the 2013 Angolan DENV-1 outbreak as the origin of our DRC study. Active genomic surveillance of DENV in Africa at the portals of entry might help early outbreak response and limit sero- and genotype spread and human disease burden. [ABSTRACT FROM AUTHOR]

Anelloviruses are a ubiquitous component of healthy human viromes and remain highly prevalent after being acquired early in life. The full extent of "anellome" diversity and its evolutionary dynamics remain unexplored. We employed in-depth sequencing of blood-transfusion donor(s)-recipient pairs coupled with public genomic resources for a large-scale assembly of anellovirus genomes and used the data to characterize global and personal anellovirus diversity through time. The breadth of the anellome is much greater than previously appreciated, and individuals harbor unique anellomes and transmit lineages that can persist for several months within a diverse milieu of endemic host lineages. Anellovirus sequence diversity is shaped by extensive recombination at all levels of divergence, hindering traditional phylogenetic analyses. Our findings illuminate the transmission dynamics and vast diversity of anelloviruses and set the foundation for future studies to characterize their biology. [Display omitted] • Anellovirus genomes assembled from longitudinal blood-transfusion cohorts • Co-infections are common, with a median of six anellovirus lineages per subject • Transmitted anellovirus lineages were observed up to 260 days post-transfusion • Recombination is a key driver in anellovirus genomic diversification Anelloviruses comprise a major component of the healthy human virome. Arze et al. describe the extensive diversity of anellovirus genomes (the "anellome") in the blood of transfusion donors and recipients and uncover the dynamics of anellovirus transmission and persistence over several months after transfusion. [ABSTRACT FROM AUTHOR]

Population dynamics can be inferred from genetic sequence data by using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume migration rates and effective population sizes to be constant through time in structured populations. When considering rates to vary through time in structured populations, the number of parameters to infer increases rapidly and the available data might not be sufficient to inform these. Additionally, it is often of interest to know what predicts these parameters rather than knowing the parameters themselves. Here, we introduce a method to  infer the predictors for time-varying migration rates and effective population sizes by using a generalized linear model (GLM) approach under the marginal approximation of the structured coalescent. Using simulations, we show that our approach is able to reliably infer the model parameters and its predictors from phylogenetic trees. Furthermore, when simulating trees under the structured coalescent, we show that our new approach outperforms the discrete trait GLM model. We then apply our framework to a previously described Ebola virus dataset, where we infer the parameters and its predictors from genome sequences while accounting for phylogenetic uncertainty. We infer weekly cases to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This approach is implemented as part of the BEAST2 package MASCOT, which allows us to jointly infer population dynamics, i.e. the parameters and predictors, within structured populations, the phylogenetic tree, and evolutionary parameters. [ABSTRACT FROM AUTHOR]