ArticleViruses2024
Emerging SARS-CoV-2 Variants in Uganda in the Era of COVID-19 Vaccination.
Article in Viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Local implementation of scalable malaria molecular surveillance in a low-resource setting: a North-South collaborative model.Malaria journal · 2026Article
- Longitudinal Epidemiology and Variant Dynamics of SARS-CoV-2 in Coastal Kenya (2020-2025): Clinical Features and Wave Patterns.Open forum infectious diseases · 2026Article
- Genomic and clinical epidemiology of SARS-CoV-2 in coastal Kenya: insights into variant circulation, reinfection, and multiple lineage importations during a post-pandemic wave.BMC global and public health · 2025Article
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
The emergence of SARS-CoV-2 variants has heightened concerns about vaccine efficacy, posing challenges in controlling the spread of COVID-19. As part of the COVID-19 Vaccine Effectiveness and Variants (COVVAR) study in Uganda, this study aimed to genotype and characterize SARS-CoV-2 variants in patients with COVID-19-like symptoms who tested positive on a real-time PCR. Amplicon deep sequencing was performed on 163 oropharyngeal/nasopharyngeal swabs collected from symptomatic patients. Genome assembly, lineage classification and phylogenetic analysis was performed using the Edge Bioinformatics pipeline version 2.4.0, Pangolin version 4.3.1 and iqtree version 2.3.6 software respectively. Of the 163 deep sequences analyzed between April 2023 and March 2024, the most common were XBB.1 lineages and sublineages (113, 69.3%), followed by JN.1* (12, 7.4%), XBB.2* (11, 6.7%) and FL* (11, 6.7%), EG* (7, 4.3%), others (BQ.1.1, FY.4.1, FY.4.1.2, GY.2.1, HK.27.1) (5, 3.1%) and CM* (4, 2.5%). XBB.1* dominated from April to July 2023; thereafter, other variants, including JN.1* were increasingly detected. There was no statistically significant association between vaccine status and lineage assignment (Fisher's exact test,
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Registered trials
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