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Tunisia’s Flu Surveillance Network Tracked Four Years of SARS-CoV-2 Evolution

October 1, 2026
in Biology
Gavin Prescott
By Gavin Prescott Scienmag Editorial Profile - Ecology and Ecosystem Dynamics
Reading Time: 5 mins read
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Tunisia’s Flu Surveillance Network Tracked Four Years of SARS-CoV-2 Evolution

Tunisia's Flu Surveillance Network Tracked Four Years of SARS-CoV-2 Evolution

Tunisia's Flu Surveillance Network Tracked Four Years of SARS-CoV-2 Evolution

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When the World Health Organization designated the Omicron variant of SARS-CoV-2 as a Variant of Concern in November 2021, most high-income countries had already built dedicated genomic sequencing pipelines to track the virus’s evolution. What remained uncertain was whether a middle-income North African nation could sustain that effort over multiple years without a bespoke pandemic-era infrastructure. A new four-year analysis from Tunisia, published in Virology Journal, offers a detailed answer: by piggybacking SARS-CoV-2 sequencing onto the country’s existing influenza sentinel surveillance network, researchers at the National Influenza and Other Respiratory Viruses Laboratory in Tunis generated 669 whole genomes between November 2021 and September 2025, capturing the full sweep of the Omicron era from the first BA.1 waves to the JN.1 descendants that dominated 2024 and 2025.

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

Cite Scienmag News

Gavin Prescott. (October 1, 2026). Tunisia’s Flu Surveillance Network Tracked Four Years of SARS-CoV-2 Evolution. Scienmag. https://scienmag.com/tunisias-flu-surveillance-network-tracked-four-years-of-sars-cov-2-evolution/

Gavin Prescott. "Tunisia’s Flu Surveillance Network Tracked Four Years of SARS-CoV-2 Evolution." Scienmag, 1 October 2026, https://scienmag.com/tunisias-flu-surveillance-network-tracked-four-years-of-sars-cov-2-evolution/. Accessed 1 October 2026.

Gavin Prescott. "Tunisia’s Flu Surveillance Network Tracked Four Years of SARS-CoV-2 Evolution." Scienmag. October 1, 2026. https://scienmag.com/tunisias-flu-surveillance-network-tracked-four-years-of-sars-cov-2-evolution/

Tags: co-detectionCovidCOVID-19 pandemic infrastructure in TunisiaCOVID-19 variant tracking in North Africagenomic surveillanceGISAIDinfluenza sentinel networkinfluenza sentinel surveillance for COVID-19integration of influenza and COVID-19 surveillanceJN.1long-term virus surveillance in middle-income countriesmonitoring COVID-19 variants over four yearsOmicronOmicron variant evolution in TunisiaPango lineagesrespiratory virusesSARS-CoV-2SARS-CoV-2 genomic sequencing in Tunisiatracking SARS-CoV-2 descendants JN.1TunisiaWHO case definitions for respiratory viruseswhole genome sequencingwhole genome sequencing of SARS-CoV-2XBB
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