Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant Rt. These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ∼0.6% median absolute error and ∼6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

Seasonal influenza (flu) viruses cause outbreaks every winter. People infected with influenza typically develop mild respiratory symptoms. But flu infections can cause serious illness in young children, older adults and people with chronic medical conditions. Infected or vaccinated individuals develop some immunity, but the viruses evolve quickly to evade these defenses in a process called antigenic drift. As the viruses change, they can re-infect previously immune people. Scientists update the flu vaccine yearly to keep up with this antigenic drift. The immune system fights flu infections by recognizing two proteins, known as antigens, on the virus's surface, called hemagglutinin (HA) and neuraminidase (NA). However, mutations in the genes encoding these proteins can make them unrecognizable, letting the virus slip past the immune system. Scientists would like to know how these changes affect the size, severity and timing of annual influenza outbreaks. Perofsky et al. show that tracking genetic changes in HA and NA may help improve flu season predictions. The experiments compared the severity of 22 flu seasons caused by the A(H3N2) subtype in the United States with how much HA and NA had evolved since the previous year. The A(H3N2) subtype experiences the fastest rates of antigenic drift and causes more cases and deaths than other seasonal flu viruses. Genetic changes in HA and NA were a better predictor of A(H3N2) outbreak severity than the blood tests for protective antibodies that epidemiologists traditionally use to track flu evolution. However, the prevalence of another subtype of influenza A circulating in the population, called A(H1N1), was an even better predictor of how severe A(H3N2) outbreaks would be. Perofsky et al. are the first to show that genetic changes in NA contribute to the severity of flu seasons. Previous studies suggested a link between genetic changes in HA and flu season severity, and flu vaccines include the HA protein to help the body recognize new influenza strains. The results suggest that adding the NA protein to flu vaccines may improve their effectiveness. In the future, flu forecasters may want to analyze genetic changes in both NA and HA to make their outbreak predictions. Tracking how much of the A(H1N1) subtype is circulating may also be useful for predicting the severity of A(H3N2) outbreaks.

Timely vaccine strain selection and genomic surveillance improves evolutionary forecast accuracy of seasonal influenza A/H3N2.

For the last decade, evolutionary forecasting models have influenced seasonal influenza vaccine design. These models attempt to predict which genetic variants circulating at the time of vaccine strain selection will be dominant 12 months later in the influenza season targeted by vaccination campaign. Forecasting models depend on hemagglutinin (HA) sequences from the WHO's Global Influenza Surveillance and Response System to identify currently circulating groups of related strains (clades) and estimate clade fitness for forecasts. However, the average lag between collection of a clinical sample and the submission of its sequence to the Global Initiative on Sharing All Influenza Data (GISAID) EpiFlu database is ∼3 months. Submission lags complicate the already difficult 12-month forecasting problem by reducing understanding of current clade frequencies at the time of forecasting. These constraints of a 12-month forecast horizon and 3-month average submission lags create an upper bound on the accuracy of any long-term forecasting model. The global response to the SARS-CoV-2 pandemic revealed that modern vaccine technology like mRNA vaccines can reduce how far we need to forecast into the future to 6 months or less and that expanded support for sequencing can reduce submission lags to GISAID to 1 month on average. To determine whether these recent advances could also improve long-term forecasts for seasonal influenza, we quantified the effects of reducing forecast horizons and submission lags on the accuracy of forecasts for A/H3N2 populations. We found that reducing forecast horizons from 12 months to 6 or 3 months reduced average absolute forecasting errors to 25% and 50% of the 12-month average, respectively. Reducing submission lags provided little improvement to forecasting accuracy but decreased the uncertainty in current clade frequencies by 50%. These results show the potential to substantially improve the accuracy of existing influenza forecasting models by modernizing influenza vaccine development and increasing global sequencing capacity.

High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains.

The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes using a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify the infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately 1 month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers 6 months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. We provide an experimental protocol (dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others. IMPORTANCE: We describe a new approach that can rapidly measure how the antibodies in human serum inhibit infection by many different influenza strains. This new approach is useful for understanding how viral evolution affects antibody immunity. We apply the approach to study the effect of repeated influenza vaccination.

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2.

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin.

Human influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population and how this heterogeneity affects virus evolution. Here, we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of two H3N2 strains, A/Hong Kong/45/2019 and A/Perth/16/2009, affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that were fixed in influenza variants after 2020 cause greater escape from sera from younger individuals compared with adults. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups and suggest approaches to understand how this heterogeneous selection shapes viral evolution.

Spike deep mutational scanning helps predict success of SARS-CoV-2 clades.

SARS-CoV-2 variants acquire mutations in the spike protein that promote immune evasion1 and affect other properties that contribute to viral fitness, such as ACE2 receptor binding and cell entry2,3. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning4 to measure how more than 9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully affected ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456 and 473; however, the antigenic effects of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however, many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.

Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences.

Pathogen genomics can provide insights into disease transmission patterns, but new methods are needed to handle modern large-scale pathogen genome datasets. Genetically proximal viruses indicate epidemiological linkage and are informative about transmission events. Here, we leverage pairs of identical sequences using 114,298 SARS-CoV-2 genomes collected via sentinel surveillance from March 2021 to December 2022 in Washington State, USA, with linked age and residence information to characterize fine-scale transmission. The location of pairs of identical sequences is highly consistent with expectations from mobility and social contact data. Outliers in the relationship between genetic and mobility data can be explained by SARS-CoV-2 transmission between postal codes with male prisons, consistent with transmission between prison facilities. Transmission patterns between age groups vary across spatial scales. Finally, we use the timing of sequence collection to understand the age groups driving transmission. This work improves our ability to characterize transmission from large pathogen genome datasets.

Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years.

Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.

Positive selection underlies repeated knockout of ORF8 in SARS-CoV-2 evolution.

Knockout of the ORF8 protein has repeatedly spread through the global viral population during SARS-CoV-2 evolution. Here we use both regional and global pathogen sequencing to explore the selection pressures underlying its loss. In Washington State, we identified transmission clusters with ORF8 knockout throughout SARS-CoV-2 evolution, not just on novel, high fitness viral backbones. Indeed, ORF8 is truncated more frequently and knockouts circulate for longer than for any other gene. Using a global phylogeny, we find evidence of positive selection to explain this phenomenon: nonsense mutations resulting in shortened protein products occur more frequently and are associated with faster clade growth rates than synonymous mutations in ORF8. Loss of ORF8 is also associated with reduced clinical severity, highlighting the diverse clinical impacts of SARS-CoV-2 evolution.

Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences.

Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number R and the dispersion parameter k have however mainly remained limited to the analysis of epidemiological clusters or contact tracing data, whose collection often proves difficult. Here, we show that clusters of identical sequences are imprinted by the pathogen offspring distribution, and we derive an analytical formula for the distribution of the size of these clusters. We develop and evaluate an inference framework to jointly estimate the reproduction number and the dispersion parameter from the size distribution of clusters of identical sequences. We then illustrate its application across a range of epidemiological situations. Finally, we develop a hypothesis testing framework relying on clusters of identical sequences to determine whether a given pathogen genetic subpopulation is associated with increased or reduced transmissibility. Our work provides tools to estimate the reproduction number and transmission heterogeneity from pathogen sequences without building a phylogenetic tree, thus making it easily scalable to large pathogen genome datasets.

Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation.

Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.

Comparative Diagnostic Utility of SARS-CoV-2 Rapid Antigen and Molecular Testing in a Community Setting.

BACKGROUND: SARS-CoV-2 antigen-detection rapid diagnostic tests (Ag-RDTs) have become widely utilized but longitudinal characterization of their community-based performance remains incompletely understood. METHODS: This prospective longitudinal study at a large public university in Seattle, WA utilized remote enrollment, online surveys, and self-collected nasal swab specimens to evaluate Ag-RDT performance against real-time reverse transcription polymerase chain reaction (rRT-PCR) in the context of SARS-CoV-2 Omicron. Ag-RDT sensitivity and specificity within 1 day of rRT-PCR were evaluated by symptom status throughout the illness episode and Orf1b cycle threshold (Ct). RESULTS: From February to December 2022, 5757 participants reported 17 572 Ag-RDT results and completed 12 674 rRT-PCR tests, of which 995 (7.9%) were rRT-PCR positive. Overall sensitivity and specificity were 53.0% (95% confidence interval [CI], 49.6%-56.4%) and 98.8% (95% CI, 98.5%-99.0%), respectively. Sensitivity was comparatively higher for Ag-RDTs used 1 day after rRT-PCR (69.0%), 4-7 days after symptom onset (70.1%), and Orf1b Ct ≤20 (82.7%). Serial Ag-RDT sensitivity increased with repeat testing ≥2 (68.5%) and ≥4 (75.8%) days after an initial Ag-RDT-negative result. CONCLUSIONS: Ag-RDT performance varied by clinical characteristics and temporal testing patterns. Our findings support recommendations for serial testing following an initial Ag-RDT-negative result, especially among recently symptomatic persons or those at high risk for SARS-CoV-2 infection.

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county.

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains.

The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we have developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes via a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately one month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals, and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers six months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. This study demonstrates the utility of high-throughput sequencing-based neutralization assays that enable titers to be simultaneously measured against many different viral strains. We provide a detailed experimental protocol (DOI: https://dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and a computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.

MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions.

The spread of infectious diseases is shaped by spatial and temporal aspects, such as host population structure or changes in the transmission rate or number of infected individuals over time. These spatiotemporal dynamics are imprinted in the genome of pathogens and can be recovered from those genomes using phylodynamics methods. However, phylodynamic methods typically quantify either the temporal or spatial transmission dynamics, which leads to unclear biases, as one can potentially not be inferred without the other. Here, we address this challenge by introducing a structured coalescent skyline approach, MASCOT-Skyline that allows us to jointly infer spatial and temporal transmission dynamics of infectious diseases using Markov chain Monte Carlo inference. To do so, we model the effective population size dynamics in different locations using a non-parametric function, allowing us to approximate a range of population size dynamics. We show, using a range of different viral outbreak datasets, potential issues with phylogeographic methods. We then use these viral datasets to motivate simulations of outbreaks that illuminate the nature of biases present in the different phylogeographic methods. We show that spatial and temporal dynamics should be modeled jointly even if one seeks to recover just one of the two. Further, we showcase conditions under which we can expect phylogeographic analyses to be biased, particularly different subsampling approaches, as well as provide recommendations of when we can expect them to perform well. We implemented MASCOT-Skyline as part of the open-source software package MASCOT for the Bayesian phylodynamics platform BEAST2.

Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic.

The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.

Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State.

BACKGROUND: Long-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county. METHODS: We assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events. RESULTS: We found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional. CONCLUSIONS: Understanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.

Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant Rt. These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ~0.6% median absolute error and ~6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.