The scale of the challenge is staggering. More than 17 million SARS-CoV-2 genome sequences have been publicly shared since the pandemic began, a figure that dwarfs anything virologists had previously contemplated. Before 2020, few researchers would have predicted that a single virus would accumulate even 100,000 genomes. When Rambaut and colleagues proposed the Pango system early in the pandemic, they anticipated that roughly 200 active lineages would suffice, warning that more would obscure rather than clarify discussion. In practice, between 200 and 1,000 distinct lineages circulated in any given month for much of the pandemic. That the system survived this explosion of data owes much to community curation and to tools such as Pangolin, Nextclade, UShER and Taxonium, which made assigning new genomes to lineages a routine computational task.

The core logic of a Pango lineage system is deceptively simple. A lineage is defined as a set of virus sequences forming an epidemiologically relevant cluster on a phylogenetic tree—specifically, all sequences descending from the most recent common ancestor of a curated set of designated reference genomes. The framework distinguishes between designation, in which high-quality sequences are formally assigned to define a lineage, and assignment, in which new genomes are classified against those definitions using phylogenetic placement, defining mutations or machine-learning classifiers. Designation is a deliberate, curated act; assignment is inferential and carries some uncertainty, which the authors argue must be acknowledged when results are interpreted, particularly for lower-quality or incomplete sequences.

What makes a cluster worthy of a name? The new framework lays out a set of signals that can justify designation, tailored to the biology and public health context of each virus. These include introduction into a new region with evidence of onward transmission, a change in phenotype, a notable constellation of mutations with possible functional relevance, recombination or reassortment, or evidence of rapid growth relative to other lineages. For SARS-CoV-2, early lineage definitions leaned heavily on geographical clustering while travel restrictions limited human movement; as global travel resumed, the criteria shifted toward growth advantage, recombination and mutations associated with immune escape. Any new lineage must also be genetically distinct from its ancestor by one or more mutations, show evidence of onward community transmission, and be supported by enough high-quality genomes to evaluate these criteria—though the exact thresholds are deliberately left flexible, to be set per virus and revised as circumstances change.

The architecture of lineage names encodes ancestry directly. Founding lineages are labelled with single letters at an initial time point chosen to capture the diversity relevant to public health; descendant sublineages append dot-separated numbers, so the 75th sublineage of BA.2 becomes BA.2.75. To keep names readable as diversity accumulates, an alias is introduced at a defined hierarchical level—after three levels for SARS-CoV-2, after two for mpox virus—replacing the prefix with the next available letter while the full ancestry is preserved in metadata. Circulating recombinants receive X-prefixed codes, as with SARS-CoV-2 lineage XBB, a recombinant of BJ.1 and BA.2.75, and designation can proceed even when the parental lineages remain uncertain. For reassorting viruses with segmented genomes, the system can be anchored to a single representative segment, typically the fastest-evolving one most relevant to transmission or antigenicity, with reassortment events in other segments capable of triggering new designations.

Critically, the authors stress that Pango is not a universal taxonomy and should not replace existing schemes. The International Committee on Taxonomy of Viruses classifies viruses down to the species rank, and below that level the field has long used a patchwork of terms—type, subtype, genotype, group and clade—each with its own conventions. A Pango system occupies an intermediate granularity: finer than persistent clade nomenclatures suited to long-term evolutionary patterns such as antigenic turnover, and coarser than phylotypes, a dot-separated notation encoding every node in a phylogeny for fine-scale local investigations such as hospital outbreaks. For endemic pathogens whose diversity is deep and largely historic, the authors caution that a fine-scale dynamic system may add complexity without public health benefit. Yet it becomes valuable when new selective pressures are expected—following vaccine or antiviral introduction, for instance—or when eradication programmes demand close tracking of contemporary transmission, as with poliovirus or measles virus.

The framework is also explicit about when it should not be used at all. Pathogens must be ‘measurably evolving’—accumulating detectable genetic diversity over the timescale of interest—for a lineage system to be informative. Many RNA viruses meet this bar, but double-stranded DNA viruses generally evolve too slowly unless host-mediated processes such as APOBEC3 editing elevate their mutation rates, as has occurred in mpox virus. Zoonotic outbreaks driven by repeated independent spillovers, such as the 2018 Lassa fever outbreak in Nigeria or re-emergences of yellow fever virus in Brazil, do not warrant lineage systems, nor do self-limiting, geographically confined transmission chains. Dynamic nomenclatures are most valuable for sustained human-to-human outbreaks spreading across multiple regions, exemplified by the global clade IIb mpox outbreak of 2022, where Pango-style lineages complemented existing clade labels. Independent spillovers giving rise to separate sustained outbreaks would each warrant their own lineage system.

To demonstrate the framework on a virus with a long history of human circulation, the team built a Pango lineage system for chikungunya virus, a mosquito-borne alphavirus first identified in 1952 and since reported in 114 countries. Working from roughly 3,300 publicly available near-full-length genomes, they filtered the data to sequences sampled from 2016 onwards and designated 21 lineages across eight founding lineages, labelled A through H. The conventional three-clade geography-based system maps onto this scheme: the West African clade corresponds to lineages A and B, the Asian clade to lineage C, and the East/Central/South African clade to lineages D through H, with twelve hierarchical sublineages defined across the founding lineages. The authors argue that geography-based labels have become misleading as phylogeographic patterns shift, and that naming conventions for chikungunya have lacked standardization. Sequences predating 2016 fall outside the system under the designation O, but can be incorporated as new founding lineages if they reappear in contemporary transmission—a flexibility illustrated by a 2021 mosquito sample from China that falls outside all current lineages.

Lessons from the pandemic loom over the entire enterprise. Pango labels proved poorly suited to public communication, which is why the World Health Organization introduced Greek-letter names for variants of concern such as Alpha and Omicron; yet after Omicron no further Greek letters were assigned, and the WHO has continued to rely on Pango lineages for tracking variants of interest and concern. The pandemic also revealed structural vulnerabilities. Global sequencing capacity remains deeply unequal, with high-income countries overrepresented in public databases while epidemiologically important regions contribute few sequences. The authors argue that designation systems must compensate by applying stricter thresholds in well-sampled countries and giving greater weight to data from underrepresented regions, and that the people closest to the data must be empowered to propose and flag new lineages through transparent public forums such as GitHub.

Perhaps the most sobering recommendation concerns sustainability. The Pango system was conceived as dynamic and time-limited, expected to be most useful for the duration of the pandemic, and the authors argue that such systems should exist only as long as they retain clear utility and support. When an epidemic pathogen transitions to endemicity, a more stable, persistent nomenclature with lower maintenance burden may serve better. Yet no organization currently exists to fund the establishment and maintenance of lineage systems, even as public health agencies, universities and industry all benefit from them. The authors propose a model that leverages distributed expertise across public health laboratories and academia, coordinated through central governance and modelled on established surveillance networks. As viral genomic data continue to shape outbreak control, they conclude, coherent and accessible nomenclature will be essential for translating complex genomic information into public health action—and the principles of utility, scalability, transparency and equity laid out here offer a foundation for building it.

Subject of Research: Generalization of the Pango viral lineage nomenclature system for genomic epidemiology and surveillance

Article Title: A generalized lineage nomenclature for viral genomic epidemiology

Article References: Colquhoun, R., Hinrichs, A. S., Hill, V., Ruis, C., Roemer, C., Pybus, O. G., Rambaut, A., & O’Toole, Á. (2026). A generalized lineage nomenclature for viral genomic epidemiology. Nature Microbiology. https://doi.org/10.1038/s41564-026-02468-3

Image Credits: AI Generated

DOI: 10.1038/s41564-026-02468-3

Keywords: Pango lineages, viral genomics, genomic epidemiology, SARS-CoV-2, chikungunya virus, phylogenetics, nomenclature, outbreak surveillance, mpox virus, viral evolution, public health, recombination

The study, led by Zaineb Hamzaoui and colleagues at Charles Nicolle Hospital and the Faculty of Medicine of Tunis, drew on a national sentinel network spanning all 24 Tunisian governorates. The network combined 85 community-based influenza-like illness sites in primary health care centers with 11 hospital-based severe acute respiratory infection departments at university hospitals, all operating under WHO 2014 case definitions. Between November 1, 2021 and September 30, 2025, the laboratory received 8,635 nasopharyngeal swabs through this system and confirmed 1,020 SARS-CoV-2 infections by real-time RT-PCR, an overall positivity rate of 11.8 percent. Under the study protocol, every positive sample with a cycle threshold value below 30 was attempted for whole-genome sequencing, a threshold chosen because higher Ct values typically yield incomplete genomes and unreliable lineage assignment.

The technical workflow relied on viral RNA extraction with the Chemagic 360 instrument, library preparation using Illumina RNA Prep with Enrichment and the Respiratory Virus Oligo Panel, and sequencing on an Illumina iSeq 100. Consensus genomes were built against the Wuhan-Hu-1 reference, quality-checked in Nextclade, and cross-validated with Genome Detective before Pango lineage and GISAID clade assignment. Of the 820 sequencing attempts, 669 genomes—81.6 percent—passed quality control and were deposited in GISAID, representing 65.6 percent of all RT-PCR-confirmed infections diagnosed at the reference laboratory during the study period. That proportion is unusually high for a middle-income setting and approaches international benchmarks for adequate genomic surveillance. Mapped read proportions were consistently high, typically at or above 0.9, lending confidence to the mutation counts derived from the data.

Geographic representativeness emerged as one of the study’s strongest findings. The researchers computed an equity index for each governorate, comparing its share of national sequences with its share of national positives, and found that the distribution of sequences closely tracked the distribution of cases, with a Pearson correlation of 0.992 across the 24 governorates. Median governorate-level sequencing coverage among RT-PCR-positive samples was 60.0 percent, with an interquartile range of 28.6 to 88.9 percent. Coverage was generally higher in densely populated coastal regions, often reaching 80 percent or more, while several central and southern areas fell below 40 percent and a few interior governorates contributed no genomes at all. The equity index flagged relative under-sampling in 12 of 24 governorates and over-sampling in one, though most imbalances occurred in governorates with very few confirmed cases, limiting their impact on the national picture.

Turnaround time—the interval between specimen collection and genome submission to GISAID—was more heterogeneous. The overall median was 99 days, with an interquartile range of 60 to 146 days, and only 43.5 percent of genomes were submitted within 90 days of collection. Nearly 15 percent of sequences experienced delays exceeding 200 days, concentrated among genomes collected in 2022 and 2023. The picture improved markedly in later years: median turnaround fell to 38 days in 2024 and 67 days in 2025, and no delays beyond 200 days were observed among genomes collected in 2024 or 2025. Within higher-volume governorates, median turnaround ranged from 22 days in Sousse to 167 days in Ariana. The authors note that international guidance recommends sharing sequence data within roughly 21 days of collection, a benchmark that accelerates variant detection and risk assessment, and they point to streamlining specimen transport and bioinformatics workflows as routes to improvement.

The lineage data reveal a strikingly dynamic viral landscape. XBB and its descendants accounted for the largest share of genomes at 25.0 percent, followed by BA.4/BA.5 lineages at 22.1 percent, JN.1 and its descendants at 13.6 percent, BA.2 at 12.6 percent, and BA.1 at 9.0 percent. Residual Delta lineages represented 8.7 percent, with the remaining 9.1 percent belonging to other lineages, including pre-Omicron variants and non-XBB recombinants. Nearly all genomes fell within the Omicron-associated GISAID clades GK and GRA. Within these broad categories, a handful of individual Pango lineages—most notably BA.2, BA.5.2, AY.122, BA.1.1, BA.5.2.20, and XBB.2.3.11—contributed the largest shares, while most Omicron sublineages each accounted for less than 2 percent of genomes, a long tail of rare variants underlying the aggregated groups.

The mutational trajectories documented in the dataset chart the virus’s continuing diversification on the Omicron backbone. Early Delta, BA.1, and BA.2 genomes carried roughly 30 to 50 amino acid substitutions relative to the Wuhan-Hu-1 reference, whereas later XBB and JN.1 sublineages frequently exceeded 70 to 80 substitutions, with some recombinants approaching 100. Amino acid deletions also accumulated progressively, reaching 18 to 19 in some JN.1-related genomes. Phylogenetic placement using the Nextclade reference tree showed that Tunisian genomes did not form country-specific outlier clusters but were fully embedded within globally circulating clades: early sequences from late 2021 clustered in Delta clade 21J, while subsequent genomes distributed across successive Omicron clades from 21K and 21L through 22B and 22E to the 23D/23E and 24A through 24H clades corresponding to XBB and JN.1 lineages. This pattern, consistent with repeated introductions rather than long-term local persistence of restricted clusters, mirrors findings from other North African and Mediterranean settings.

Epidemiological curves tied each wave of positivity to the establishment or replacement of a dominant lineage. Testing volumes rose recurrently each winter, peaking in January 2023, but SARS-CoV-2 positivity followed its own rhythm, with substantial peaks in mid-2022 and mid-2023 alongside winter surges. The BA.5.2 wave crested in May through July 2022, the recombinant XBB.2.3.11 drove a surge in July and August 2023, and a JN.1 wave in winter 2023-2024 gave way to extensive circulation of JN.1 offspring through 2024 and 2025. The authors emphasize that these off-season outbreaks underscore that SARS-CoV-2 has not yet settled into the strict winter seasonality of influenza and can still generate waves whenever new immune-evasive variants emerge. The dynamics broadly paralleled those observed across North Africa, the Eastern Mediterranean, and Southern Europe, where KP.3.1.1 and related JN.1 descendants predominated by mid-2024.

Because the sequencing platform sat within a multiplex respiratory surveillance framework, the study also captured viral co-detections. Among the 669 sequenced SARS-CoV-2-positive samples, 92—13.8 percent—showed co-detection with at least one other respiratory virus. Rhinovirus was the most frequent companion at 2.8 percent, followed by adenovirus at 1.5 percent, influenza A/H3N2 at 1.3 percent, and respiratory syncytial virus at 1.2 percent. Influenza viruses were involved in 16 co-detections, or 2.4 percent of sequenced cases. The predominance of rhinovirus aligns with pandemic-era literature showing that rhinovirus and enterovirus persisted or re-emerged earlier than enveloped respiratory viruses, which were markedly suppressed during the first phase of the COVID-19 pandemic. Influenza detections, absent during the 2020-2021 season, resumed once broader multiplex testing was reintroduced from the 2021-2022 season onward.

The authors are candid about the limitations. Clinical metadata were available for only 46.5 percent of sequenced cases, and age data were missing for 8.8 percent of patients, with completeness varying by year and governorate. The Ct-based inclusion criterion may have biased the cohort toward higher viral load infections and earlier sampling timepoints, and the phylogenetic component relied on reference-tree placement rather than de novo reconstruction, precluding formal inference of transmission clusters or introduction counts. The dataset also reflects the ILI/SARI sentinel sampling frame rather than the entirety of national COVID-19 testing. Even so, the study demonstrates that an influenza-based sentinel network in a middle-income country can deliver sustained, geographically near-proportional SARS-CoV-2 genomic surveillance at high sequencing quality. By combining near-proportional sampling across governorates with multipathogen testing, the Tunisian platform offers a scalable model for tracking future SARS-CoV-2 variants and other emerging respiratory pathogens, positioning integrated sentinel surveillance as a pragmatic foundation for pandemic preparedness in the region.

Subject of Research: Genomic surveillance of SARS-CoV-2 Omicron lineage dynamics in Tunisia

Article Title: Genomic surveillance of SARS-CoV-2 in Tunisia during the omicron era: insights from the national influenza & other respiratory viruses laboratory

Article References: Hamzaoui, Z., Ferjani, S., Bouchouicha, T., Charaa, L., Landolsi, I., Medini, I., Ben Ali, R., Chammam, S., Abid, S., Kanzari, L., Ben Sassi, M., Trabelsi, S., & Boutiba Ben Boubaker, I. (2026). Genomic surveillance of SARS-CoV-2 in Tunisia during the omicron era: insights from the national influenza & other respiratory viruses laboratory. Virology Journal, 23(1), Article 215. https://doi.org/10.1186/s12985-026-03262-7

Image Credits: AI Generated

DOI: 10.1186/s12985-026-03262-7

Keywords: SARS-CoV-2, Omicron, genomic surveillance, Tunisia, whole-genome sequencing, Pango lineages, GISAID, JN.1, XBB, respiratory viruses, influenza sentinel network, co-detection