Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 348

The DOI matches its Crossref record.

Epidemiological benefits of more-effective tuberculosis vaccines, drugs, and diagnostics

Open access
Abu-Raddad, Laith J., Sabatelli, Lorenzo, Achterberg, Jerusha T., Sugimoto, Jonathan D., Longini, Ira M., Dye, Christopher, Halloran, M. Elizabeth·2009·Proceedings of the National Academy of Sciences

Abstract

The Bill and Melinda Gates Foundation supports an ambitious portfolio of novel vaccines, drug regimens, and diagnostic tools for tuberculosis (TB). We elicited the expected efficacies and improvements of the novel interventions in discussions with the foundations managing their development. Using an age-structured mathematical model of TB, we explored the potential benefits of novel interventions under development and those not yet in the portfolio, focusing on the WHO Southeast Asia region. Neonatal vaccination with the portfolio vaccine decreases TB incidence by 39% to 52% by 2050. Drug regimens that shorten treatment duration and are efficacious against drug-resistant strains reduce incidence by 10–27%. New diagnostics reduce incidence by 13–42%. A triple combination of a portfolio vaccine, drug regimen, and diagnostics reduces incidence by 71%. A short mass vaccination catch-up campaign, not yet in the portfolio, to augment the triple combination, accelerates the decrease, preventing >30% more cases by 2050 than just the triple combination. New vaccines and drug regimens targeted at the vast reservoir of latently infected people, not in the portfolio, would reduce incidence by 37% and 82%, respectively. The combination of preventive latent therapy and a 2-month drug treatment regimen reduces incidence by 94%. Novel technologies in the pipeline would achieve substantial reductions in TB incidence, but not the Stop TB Partnership target for elimination. Elimination will require new delivery strategies, such as mass vaccination campaigns, and new products targeted at latently infected people.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.0901720106

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 348

Metadata

Authors
Abu-Raddad, L. J., Sabatelli, L., Achterberg, J. T., Sugimoto, J. D., Longini, I. M., Dye, C., & Halloran, M. E.
Venue
Proceedings of the National Academy of Sciences
Published
2009-08-18
Volume
106
Issue
33
Pages
13980-13985
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2047509792
Cited by
348
References
0
FWCI
16.75
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Epidemiological benefits of more-effective tuberculosis vaccines, drugs, and diagnostics. https://app.cochat.ai/papers/10.1073/pnas.0901720106
The paper
Abu-Raddad, L. J., Sabatelli, L., Achterberg, J. T., Sugimoto, J. D., Longini, I. M., Dye, C., & Halloran, M. E. (2009). Epidemiological benefits of more-effective tuberculosis vaccines, drugs, and diagnostics. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0901720106

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.