Evidence map›Paper›PMID 41678562›Full record

ArticlePLoS medicine2026

Estimating the population impact of new tuberculosis vaccines depending on efficacy against infectious asymptomatic tuberculosis: A modelling study.

Hira Tanvir, Rebecca A Clark, Tom Sumner, Katherine C Horton, Tomos O Prŷs-Jones, Roel Bakker, Kirankumar Rade, Vidya Mave, Mark Hatherill, Gavin J Churchyard and 2 more

Abstract read
In one paragraph

Article in PLoS medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Hira TanvirTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0009-0006-5358-6932
Rebecca A ClarkTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0001-8923-0301
Tom SumnerTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0001-9305-534X
Katherine C HortonTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0002-7542-4065
Tomos O Prŷs-JonesTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0001-6472-302X
Roel BakkerTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0002-1958-0944
Kirankumar RadeIndependent Consultant, New Delhi, India.ORCID https://orcid.org/0000-0001-7601-0780
Vidya MaveByramjee-Jeejeebhoy Government Medical College, Johns Hopkins University Clinical Research Site, Pune, India.ORCID https://orcid.org/0000-0001-8509-4517
Mark HatherillSouth African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, University of Cape Town, Cape Town, South Africa.ORCID https://orcid.org/0000-0003-3491-1809
Gavin J ChurchyardAurum Institute NPC, Johannesburg, South Africa.ORCID https://orcid.org/0000-0002-4269-3699
Rein M G J HoubenTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0003-4132-7467
Richard G WhiteTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0003-4410-6635

Funding

Aurum Clinical Trials UnitUM1AI154463 · NIAID · AURUM INSTITUTE NPC · PI Gavin John Churchyard · 2021 to 2026
$8.1M
NIAID NIH HHS UM1 AI154463
6 · The paper itself

Abstract

backgroundTuberculosis (TB) remains a leading cause of infectious disease death. New TB vaccines are currently in late-stage trials and may be available before the end of the decade. Modelling predicts new TB vaccines may reduce global burden but rely on assumptions about vaccine efficacy by TB disease stage and TB natural history, which may be incorrect. We explored the sensitivity of estimates of the impact of new TB vaccines to uncertainties in efficacy by disease stage and natural history. METHODS AND

findingsWe developed a dynamic compartmental TB model for India, including early TB disease stages (non-infectious disease, infectious asymptomatic disease, and infectious symptomatic disease). Scenarios assumed 50% vaccine efficacy for 10 years and prevented progression to (a) only infectious symptomatic disease, or (b) any infectious disease (infectious asymptomatic disease and infectious symptomatic disease), or (c) any disease (non-infectious disease, infectious asymptomatic disease, and infectious symptomatic disease). We estimated impact on averting disease episodes over 2030-2050, compared to no-new-vaccine introduction. Results suggest, over 3 years, there was little difference in the proportion of cumulative symptomatic disease episodes averted by vaccines preventing only infectious symptomatic disease, any infectious disease, or any disease (1.6%, 2.3%, and 2.3%, respectively). However, over 20 years, compared to vaccines preventing only infectious symptomatic disease, vaccines preventing any infectious disease, or any disease, averted a markedly higher proportion of symptomatic disease episodes (7.3%, 19.4%, and 23.3%, respectively), due to preventing continued transmission from infectious asymptomatic disease. A key limitation with any mathematical modelling study is the uncertainty associated with the inputs, and further data collection is required to better understand the transmissibility, morbidity, and dynamics of asymptomatic disease, to improve modelling estimates and inform wider policy.

conclusionsOur modelling estimates that the population impact of new TB vaccines may depend on efficacy against infectious asymptomatic disease. TB vaccine trials should include analyses of participant sputum samples (collected during or at the end of trials and analysed at the end of trials) to enable better estimates of the potential value of new TB vaccines against infectious asymptomatic disease.

Indexed as

Asymptomatic InfectionsTuberculosisTuberculosis VaccinesVaccine EfficacyHumansIndiaTuberculosis Vaccines

Identifiers

PMID41678562
PMCPMC12928567

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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.