Evidence map›Paper›PMID 40463570›Full record

ArticlemedRxiv : the preprint server for health sciences2025

The

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

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. 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.

Sandip MandalCenter for Modeling and Analysis, Avenir Health, Glastonbury, CT, USA.ORCID 0000-0001-5651-8632
Srinath SatyanarayanaCenter for Modeling and Analysis, Avenir Health, Glastonbury, CT, USA.ORCID 0000-0002-5420-2551
Finn McQuaidTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.ORCID 0000-0001-6199-0931
Peter J DoddDivision of Population Health, School of Medicine & Population Health, University of Sheffield, UK.ORCID 0000-0001-5825-9347
Nicolas A MenziesDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-2571-016X
Richard G WhiteTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Nimalan ArinaminpathyWorld Health Organization, Geneva, Switzerland.
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 and Tropical Medicine, London, UK.
David W DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Mikaela SmitThe Global Fund, Geneva, Switzerland.ORCID 0000-0001-8530-748X
Suvanand SahuStop TB Partnership, Geneva, Switzerland.
Carel PretoriusCenter for Modeling and Analysis, Avenir Health, Glastonbury, CT, USA.ORCID 0009-0007-9223-4013

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
Gates Foundation INV-035506NIAID NIH HHS R01 AI147321Wellcome Trust
6 · The paper itself

Abstract

Background: Tuberculosis (TB) remains one of the deadliest infectious diseases globally. Despite the World Health Organization's (WHO) End TB Strategy targets for 2035, progress has been hindered by structural, financial, and implementation barriers, including recent cuts in global funding. Strategic use of mathematical modelling is useful for prioritizing high-impact interventions and optimizing limited resources. A new global TB infection transmission model was developed to address limitations in existing tools with respect to these applications. Methods: The model includes enhanced features such as age-specific mixing, explicit representation of asymptomatic TB, stratification by drug resistance, HIV status, and new vaccine status, and inclusion of both public and private care pathways. It was calibrated to country-specific data using Bayesian adaptive Markov Chain Monte Carlo (MCMC) methods. The model was used to assess the impact of national strategic plans and the Global Plan to End TB, using a Target Population (TP) component to map interventions to WHO guidelines. Results: Model calibration showed good agreement with historical TB data from 29 high-burden countries, with case studies for Indonesia and Nigeria presented here. In Indonesia, comprehensive implementation of Global Plan interventions - including public-private mix efforts, modern diagnostics, improved treatment for drug-resistant TB, and a post-exposure vaccine - could enable the country to achieve End TB targets by 2035. In Nigeria, implementing its National Strategic Plan could reduce TB incidence by 27% and mortality by 37% by 2030, even without a vaccine. The model highlighted the additional efforts that are needed to meet the End-TB goals. Conclusions: The enhanced TB model provides a flexible, policy-relevant framework for assessing the epidemiological impact of TB interventions at both national and global levels. Its open-source design and alignment with WHO recommendations make it a valuable tool for guiding evidence-based investments amid tightening global health budgets.

Identifiers

PMID40463570
PMCPMC12132130

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.