ArticleBMC public health2025
Adaptive modeling of HIV-TB coinfection dynamics and intervention optimization.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe syndemic of HIV and tuberculosis (TB) co-infection remains a critical global health challenge, particularly in resource-limited settings where conventional epidemiological models fail to capture the complex evolutionary dynamics between pathogens, hosts, and interventions. Current approaches lack adaptive mechanisms to account for temporal changes in transmission parameters and quality of life (QoL) impacts, creating an urgent need for innovative modeling frameworks.
methodsThis study focuses on the Khyber Pakhtunkhwa province of Pakistan, a region with moderate HIV prevalence and high TB incidence. The analysis used de-identified clinical and demographic data (N = 592) collected from tertiary hospitals in KP between 2021 and 2023. We developed a novel hybrid modeling approach integrating empirical clinical data with evolutionary computation through three synergistic components: (1) evolutionary-optimized demographic sampling ([Formula: see text]), (2) time-varying compartmental modeling with adaptive parameters ([Formula: see text]), and (3) multi-objective intervention optimization. The framework was validated through a four-pillar approach incorporating statistical metrics, evolutionary cross-validation, clinical evaluation, and policy impact assessment. KEY
resultsOur analysis revealed three critical findings: First, transmission parameters exhibited distinct temporal patterns, with TB showing saturating growth ([Formula: see text]) while HIV declined gradually. Second, gender-specific exposure dynamics were identified, with males having significantly higher transmission risk ([Formula: see text], [Formula: see text]). Third, the targeted treatment strategy emerged as optimally cost-effective (ICER = $2,300/quality-adjusted life years (QALY), 95% CI: 1,850-2,750), reducing [Formula: see text] by 54% while maintaining high feasibility (0.91). SIGNIFICANCE: This study provides the first comprehensive framework that simultaneously addresses pathogen evolution, host dynamics, and intervention optimization in HIV-TB co-infection. The findings offer actionable insights for public health policy, particularly in balancing cost-effectiveness with implementation feasibility. Our evolutionary-optimized approach establishes a new paradigm for modeling complex disease systems, with potential applications extending beyond HIV-TB to other interacting epidemics.
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.