A Multi-Strain Tuberculosis Transmission Model With Treatment Stratification and Neural Networks
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Alhassan Ibrahim, Usa Wannasingha Humphries, Zainab Aliyu Attahir, Rahat Zarin
Publisher: Wiley
Publication year: 2025
Start page: 1
End page: 20
Number of pages: 20
eISSN: 2513-0390
URL: https://advanced.onlinelibrary.wiley.com/doi/10.1002/adts.202501393
Languages: English-Great Britain (EN-GB)
Abstract
| Tuberculosis (TB) remains a major global health threat, made worse by the emergence of multidrug-resistant TB (MDR-TB). This study presents a multi-strain transmission model for TB that incorporates drug-susceptible |
(DS-TB) and multidrug-resistant (MDR-TB) strains. The model introduces key
innovations: waning vaccine immunity, separate treatment pathways for
| latent and active TB, and endogenous development of drug resistance. The system’s dynamics are analyzed and control strategies are identified. The methodology combined a deterministic compartmental model with an artificial neural network (ANN) trained via the Levenberg-Marquardt |
algorithm (LMB) to enhance computational efficiency. The basic reproduction
numbers are calculated as R 0D = 3.0923 for DS-TB and R0M = 2.4490 for
| MDR-TB, confirming the endemic potential of both strains. A global sensitivity analysis using Latin Hypercube Sampling and Partial Rank Correlation Coefficients (LHS-PRCC) quantified key drivers: a 10% increase in the DS-TB |
transmission rate 𝜷 raised R0D by approximately 9.6%. In contrast, a 10%
increase in the treatment initiation rate for active DS-TB 𝜸1 reduced it by 9.6%,
| with similar effects for MDR-TB. These results provide a quantitative tool for policymakers, demonstrating that prioritizing rapid diagnosis and treatment of active cases offers the most effective strategy for immediate TB reduction. |
Keywords
basic reproduction number, machine learning, public health policy, sensitivity analysis, TB
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