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Emerging variants of SARS-CoV-2 are characterized and monitored closely by national genomic surveillance. In addition to sequencing efforts from US public health, academic, and commercial laboratories, the Centers for Disease [...]

The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5–98.0%) and 72.7% (95% CI, 43.4–90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model—33.3% (95% CI, 12.1–64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines. [ABSTRACT FROM AUTHOR]

An outbreak of pneumonia of unknown etiology in Wuhan, China, was reported to the World Health Organization on December 31, 2019 (1). Researchers determined that the illness, later known as [...]

Background. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. Methods. We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study. Results. The benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2. Discussion. The datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019, and the outbreak rapidly evolved into the current coronavirus disease pandemic. SARS-CoV-2 is a respiratory virus that causes symptoms similar to those caused by influenza A and B viruses. On July 2, 2020, the US Food and Drug Administration granted emergency use authorization for in vitro diagnostic use of the Influenza SARS-CoV-2 Multiplex Assay. This assay detects influenza A virus at 102.0, influenza B virus at 102.2, and SARS-CoV-2 at 100.3 50% tissue culture or egg infectious dose, or as few as 5 RNA copies/reaction. The simultaneous detection and differentiation of these 3 major pathogens increases overall testing capacity, conserves resources, identifies co-infections, and enables efficient surveillance of influenza viruses and SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.

Importance: The COVID-19 pandemic was accompanied by an unprecedented surveillance effort. The resulting data were and will continue to be critical for surveillance and control of SARS-CoV-2. However, some genomic surveillance methods experienced challenges as the virus evolved, resulting in incomplete and poor quality data. Complete and quality coverage, especially of the S-gene, is important for supporting the selection of vaccine candidates. As such, we developed a robust method to target the S-gene for amplification and sequencing. By focusing on the S-gene and imposing strict coverage and quality metrics, we hope to increase the quality of surveillance data for this continually evolving gene. Our technique is currently being deployed globally to partner laboratories, and public health representatives from 79 countries have received hands-on training and support. Expanding access to quality surveillance methods will undoubtedly lead to earlier detection of novel variants and better inform vaccine strain selection., Competing Interests: The authors declare no conflict of interest.

CDC has used national genomic surveillance since December 2020 to monitor SARS-CoV-2 variants that have emerged throughout the COVID-19 pandemic, including the Omicron variant. This report summarizes U.S. trends in variant proportions from national genomic surveillance during January 2022-May 2023. During this period, the Omicron variant remained predominant, with various descendant lineages reaching national predominance (>50% prevalence). During the first half of 2022, BA.1.1 reached predominance by the week ending January 8, 2022, followed by BA.2 (March 26), BA.2.12.1 (May 14), and BA.5 (July 2); the predominance of each variant coincided with surges in COVID-19 cases. The latter half of 2022 was characterized by the circulation of sublineages of BA.2, BA.4, and BA.5 (e.g., BQ.1 and BQ.1.1), some of which independently acquired similar spike protein substitutions associated with immune evasion. By the end of January 2023, XBB.1.5 became predominant. As of May 13, 2023, the most common circulating lineages were XBB.1.5 (61.5%), XBB.1.9.1 (10.0%), and XBB.1.16 (9.4%); XBB.1.16 and XBB.1.16.1 (2.4%), containing the K478R substitution, and XBB.2.3 (3.2%), containing the P521S substitution, had the fastest doubling times at that point. Analytic methods for estimating variant proportions have been updated as the availability of sequencing specimens has declined. The continued evolution of Omicron lineages highlights the importance of genomic surveillance to monitor emerging variants and help guide vaccine development and use of therapeutics., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.

Monitoring emerging SARS-CoV-2 lineages and their epidemiologic characteristics helps to inform public health decisions regarding vaccine policy, the use of therapeutics, and health care capacity. When the SARS-CoV-2 Alpha variant emerged in late 2020, a spike gene (S-gene) deletion (Δ69-70) in the N-terminal region, which might compensate for immune escape mutations that impair infectivity (1), resulted in reduced or failed S-gene target amplification in certain multitarget reverse transcription-polymerase chain reaction (RT-PCR) assays, a pattern referred to as S-gene target failure (SGTF) (2). The predominant U.S. SARS-CoV-2 lineages have generally alternated between SGTF and S-gene target presence (SGTP), which alongside genomic sequencing, has facilitated early monitoring of emerging variants. During a period when Omicron BA.5-related sublineages (which exhibit SGTF) predominated, an XBB.1.5 sublineage with SGTP has rapidly expanded in the northeastern United States and other regions., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No other potential conflicts of interest were disclosed.