Evidence map›Paper›PMID 37234098›Full record

ArticleInfectious Disease Modelling2023

Studying the efficacy of isolation as a control strategy and elimination of tuberculosis in India: A mathematical model.

Archana Singh Bhadauria, Hom Nath Dhungana, Vinay Verma, Stephen Woodcock, Tapan Rai

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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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

8 citing papers in PubMed.

  1. Socioeconomic Interventions for WHO's End TB Strategy Targets: Insights from SIR Modelling in Kazakhstan.International journal of environmental research and public health · 2026
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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

5 authors.

Archana Singh BhadauriaDepartment of Mathematics and Statistics, Deen Dayal Upadhyaya Gorakhpur University, India.
Hom Nath DhunganaSchool of Mathematical and Physical Sciences, University of Technology Sydney, Australia.
Vinay VermaFaculty of Mathematical and Statistical Sciences, Institute of Natural Sciences and Humanities, Shri Ramswaroop Memorial University, Uttar Pradesh, India.
Stephen WoodcockSchool of Mathematical and Physical Sciences, University of Technology Sydney, Australia.
Tapan RaiSchool of Mathematical and Physical Sciences, University of Technology Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

India has the highest burden of both tuberculosis (TB) and multidrug-resistant TB (MDR-TB) based on the WHO Global TB Report 2019. Although the available data suggest that the total TB incidence has declined, the absolute number of new cases is still increasing. The number of reported TB cases in India in 2018 was 2.2 million, which was 1.5 million in 2009. About 47% increment in TB case notification in India within a decade shows a persistent public health problem. India contributes about 22% of the World's TB burden. Indian National Strategic Plan 2017-2025, sets out the government plans to eliminate TB by 2025. However, the milestone seems unrealistic to achieve the TB eradication goal by 2025. We developed a five-dimensional mathematical model to understand the TB dynamics in India and investigate the possibility of the earliest TB eradication time frame. The model stratifies the entire TB class into three different classes as drug-sensitive (DS), MDR, and isolated classes. The effective reproduction number, equilibrium points, and stability analysis of the model were carried out. This model predicts the total estimated cases of DS-TB and MDR-TB from 2018 to 2035 through numerical simulation and suggests that TB may be eliminated by 2035 in India if the treatment success rate could be achieved to 95%, by contact tracing and isolating at least 50% of MDR-TB.

Indexed as

3437Drug-sensitiveIsolationMathematical modellingMulti-drug resistantReproduction number

Identifiers

PMID37234098
PMCPMC10206434

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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.