Evidence map›Paper›PMID 42516122›Full record

ArticleBulletin of the World Health Organization2026

A model for the epidemiological impact of tuberculosis policy options.

Sandip Mandal, Srinath Satyanarayana, Finn McQuaid, Peter J Dodd, Nicolas A Menzies, Richard G White, Nimalan Arinaminpathy, Rein Mgj Houben, David W Dowdy, Mikaela Smit and 2 more

Abstract read
In one paragraph

Article in Bulletin of the World Health Organization, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. A model for the epidemiological impact of tuberculosis policy options.Bulletin of the World Health Organization · 2026
    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

12 authors.

Sandip MandalCenter for Modeling and Analysis, Avenir Health, 2510 Main Street, Glastonbury, CT 06033, United States of America (USA).
Srinath SatyanarayanaCenter for Modeling and Analysis, Avenir Health, 2510 Main Street, Glastonbury, CT 06033, United States of America (USA).
Finn McQuaidDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, England.
Peter J DoddSchool of Medicine and Population Health, University of Sheffield, Sheffield, England.
Nicolas A MenziesDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, USA.
Richard G WhiteDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, England.
Nimalan ArinaminpathySchool of Public Health, Imperial College London, London, England.
Rein Mgj HoubenDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, England.
David W DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
Mikaela SmitGlobal Fund, Geneva, Switzerland.
Suvanand SahuStop TB Partnership, Geneva, Switzerland.
Carel PretoriusCenter for Modeling and Analysis, Avenir Health, 2510 Main Street, Glastonbury, CT 06033, United States of America (USA).

Funding

Defining drivers of TB transmission in the era of universal ART, and implications for finding the walking wellR01AI147321 · NIAID · LONDON SCH/HYGIENE & TROPICAL MEDICINE · PI GRANT, ALISON · 2019 to 2024
$2.3M
Bill & Melinda Gates Foundation INV-035506NIAID NIH HHS R01 AI147321Wellcome Trust
6 · The paper itself

Abstract

Objective: To develop a new tuberculosis transmission model, addressing the limitations of and building on the TB Impact Model and Estimates software tool, to enable decision-makers to assess the impact of various tuberculosis interventions and allocate resources more effectively. Methods: We designed a model incorporating diagnosis and treatment pathways across public and private sectors, stratified across age groups, drug susceptibility, human immunodeficiency virus status and vaccination status. We calibrated our model using country-specific data from 29 high-burden countries and determined calibration target indicators according to national epidemic profiles. We performed the model calibration using a Bayesian adaptive Markov chain Monte Carlo process. We compare modelled and actual data for Indonesia and Nigeria. Findings: Our model calibration results showed good agreement with historical tuberculosis data. In Indonesia, we demonstrate that comprehensive implementation of the Stop TB Partnership Conclusion: Our model provides a robust analytical foundation from which to assess the epidemiological impact of diverse interventions, prioritize investments and guide policy. The model's open-source design and alignment with WHO recommendations make it a valuable tool for guiding evidence-based investment.

Indexed as

Epidemiological ModelsHealth PolicyTuberculosisAntitubercular AgentsBayes TheoremHumansIndonesiaMarkov ChainsNigeriaAntitubercular Agents

Identifiers

PMID42516122
PMCPMC13404388

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.