{"doi":"10.1093/ve/veab053","title":"Coordinating SARS-CoV-2 genomic surveillance in the United States","abstract":"The United States has rapidly responded to the emergence of new severe acute respiratory syndrome coronavirus 2 variants of concern by scaling up genomic surveillance. Tens of thousands of viral genomes are now sequenced in American labs each week to track the spread of variants originating in the United States (Annavajhala et al. 2021; Deng et al. 2021) or imported from other countries (Washington et al. 2021) to keep diagnostics, therapeutics, and vaccines up to date (Walensky, Walke, and Fauci 2021). An influx of Federal funding provides an unprecedented opportunity to build a new US genomic surveillance system from the ground up, informed by in-country expertise (National Academies of Sciences 2020; Black et al. 2020) as well as existing models of successful genomic surveillance systems established in other countries (COVID-19 Genomics UK (COG-UK) 2020; Seemann et al. 2020; Msomi, Mlisana, and Tulio 2020). Fully leveraging genetic data require a centrally coordinated national sampling strategy and consortiums for sharing valuable metadata, which are needed to study how new variants evade host immunity, cause severe disease, or transmit differently in human populations. However, US public and private labs have a history of autonomy and strong protections for patient privacy, presenting ongoing barriers to central coordination and data sharing. Routine, population-based sampling that provides an unbiased, representative survey of the genetic composition of viruses circulating over time and space is the gold standard for tracking how new variants relate to disease severity, population immunity, and epidemic trajectory. Instead, genomic surveillance is frequently performed opportunistically for practical reasons, introducing biases that limit the downstream utility of the data. America’s vast network of public and private labs have independently generated large numbers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes that provide highly resolved pictures of the genetic diversity and transmission chains underlying local epidemics (Bedford et al. 2020; Chu et al. 2020; Gonzalez-Reiche et al. 2020; Lemieux et al. 2021). But these pursuits often target local or hospital-based populations with detailed patient metadata and do not always align with broader population-based surveillance of national interest. Coordinating the efforts of America’s diverse networks of state, commercially run, and academic labs within a nationwide surveillance consortium that standardizes population-based sampling is no small feat, but the success of the genomics program hinges on it. Carrots work better than sticks and one reason the UK consortium has been successful is because participants access user-friendly, customizable tools for visualizing local and national data trends over time and space (Argimón et al. 2016; Nicholls et al. 2020). Both cloud-hosted and locally implemented bioinformatics tools enable quick conversion from unprocessed sequence data to deposition in global data platforms (Connor et al. 2016; Grubaugh et al. 2019; Singer et al. 2020; Rambaut et al. 2020). Local officials also see benefits when real-time genomic data explain the necessity of unpopular policy reversals, such as the school closures that followed the spike of highly transmissible B.1.1.7 variants in the UK in December 2020 (Volz et al. 2021). High throughput bioinformatic pipelines allow state and local public health labs to flag new variants of concern (VOCs) emerging in communities (Hadfield et al. 2018). But fully leveraging genetic data to understand variants’ epidemiological impact requires expertise in advanced phylodynamic methods that are still far from being automated (Lemey et al. 2020; du Plessis et al. 2021). Years ago, the Federal government had the foresight to establish two highly successful research networks, Research and Policy for Infectious Disease Dynamics and Models of Infectious Disease Agent Study, with experts in infectio","journal":"Virus Evolution","year":2021,"id":206196,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.916,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":79033,"name":"Peter Thielen","orcid":"0000-0003-1807-2785","position":1,"is_corresponding":false},{"id":228542,"name":"Martha I. Nelson","orcid":"0000-0003-4814-0179","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-18T23:51:37.630221Z","pmid":"34527283","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}