ArticleBMC infectious diseases2026
Modelling TB-HIV co-infection and evaluating intervention strategies in Thailand.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Using interpretable decision trees to explore TB-HIV integration under data constraints: a proof-of-concept analysis.Frontiers in tuberculosis · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The co-infection of Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) remains a significant public health challenge both in Thailand and globally. This dual burden exacerbates disease severity, complicates treatment strategies, and increases mortality rates. In this study, we developed a mathematical model of TB-HIV co-infection to examine the dynamics of TB prevalence, HIV prevalence, co-infection prevalence and combined (aggregated) disease burden across Thailand. The model was calibrated using real-world incidence data of TB-HIV co-infection in the country. The global sensitivity analysis revealed that the transmission rates of TB and HIV had the most significant influence on the basic reproduction numbers, [Formula: see text] and [Formula: see text], highlighting critical targets for intervention. The model was further used to evaluate the potential impact of various intervention strategies on reducing the prevalence of TB, HIV, co-infection, and combined disease burden over the period 2022-2039. We found that increasing treatment coverage for TB patients reduces TB prevalence, while HIV prevalence and TB-HIV co-infection prevalence increase over time due to the prolonging the survival of HIV-positive individuals and improving case detection. Additionally, treating individuals co-infected with TB and HIV significantly lowers the prevalence of co-infections. Our findings further suggest that reducing the progression rates of both TB and HIV leads to more substantial and sustained reductions in TB prevalence, HIV prevalence, and TB-HIV co-infection prevalence in Thailand. Our findings also indicate that higher HIV acquisition among TB patients amplifies the burden of co-infection, whereas increased TB acquisition among HIV patients accelerates its progression. Therefore, targeting the dual burden of TB and HIV is essential for lower the prevalence of both diseases, protect vulnerable populations, and ultimately work towards eliminating the diseases as a public health threat.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.