Evidence map›Paper›PMID 41631698›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2026

Integrating Genomic Data into Test-negative Designs for Estimating Lineage-specific COVID-19 Vaccine Effectiveness.

Kevin C Ma, Diya Surie, Natalie E Dean, Clinton R Paden, Natalie J Thornburg, Fatimah S Dawood

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Kevin C MaNational Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.ORCID 0000-0002-4326-2911
Diya SurieNational Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.ORCID 0000-0003-3931-1423
Natalie E DeanRollins School of Public Health, Emory University, Atlanta, Georgia, USA.ORCID 0000-0003-3884-0921
Clinton R PadenNational Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.
Natalie J ThornburgNational Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.ORCID 0000-0003-3523-3616
Fatimah S DawoodNational Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, USA.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

backgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage-specific coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) studies can inform decision-making on whether vaccine composition updates are needed to maintain effectiveness against severe disease as SARS-CoV-2 continues to evolve. Lineage assignment methods in VE test-negative design (TND) studies include sequence-based (whole-genome sequencing), proxy-based (eg, S-gene target failure during polymerase chain reaction), and time period-based (using variant predominance thresholds) approaches.

methodsWe first summarize benefits, challenges (including cost and timeliness), and methodologic considerations for estimating lineage-specific COVID-19 VE using these different assignment approaches. We then use a deterministic model to illustrate the potential impact of lineage misclassification error on VE estimates in a TND using period-based versus sequence-based lineage assignment across different variant emergence scenarios.

resultsOur model suggests period-based analyses may sometimes underestimate differences in VE between 2 lineages due to lineage misclassification error. This effect is most evident during prolonged variant cocirculation or in early time periods following new variant takeover. Using higher predominance thresholds can reduce VE estimate bias in period-based analyses but at the expense of sample size, reducing precision or outright precluding estimation under some scenarios. Period-based VE analyses should therefore consider including sensitivity analyses to characterize robustness of VE estimates to different predominance thresholds.

conclusionsTND studies using sequence-, proxy-, and period-based lineage assignment have contributed substantially toward understanding SARS-CoV-2 variant-mediated vaccine escape, but biases that can affect each study design vary. Our results identify analytic considerations for robust estimation and suggest principles that may translate to other pathogens that undergo continuous antigenic drift.

Indexed as

COVID-19COVID-19 VaccinesSARS-CoV-2Vaccine EfficacyGenome, ViralGenomicsHumansWhole Genome SequencingCOVID-19 VaccinesCOVID-19SARS-CoV-2test-negative designvaccine effectivenessvariant

Identifiers

PMID41631698
PMCPMC13196292

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.