Mathematical Analysis of the Transmission Dynamics of Malaria and Tuberculosis Co‐Infection With Control Strategies
ABSTRACT Co‐infections such as malaria and tuberculosis pose significant public health challenges, particularly in regions where both diseases are endemic. Despite the global burden of these infections, their combined transmission dynamics remain poorly understood, highlighting the importance of thi...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.70210 |
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| Summary: | ABSTRACT Co‐infections such as malaria and tuberculosis pose significant public health challenges, particularly in regions where both diseases are endemic. Despite the global burden of these infections, their combined transmission dynamics remain poorly understood, highlighting the importance of this study. We develop a comprehensive mathematical model that captures the complex interactions between malaria and tuberculosis within a human population. By decomposing the system into disease‐specific sub‐models, we conduct a rigorous theoretical analysis of their individual and joint behaviors. A key result of this study is the identification of backward bifurcation in the co‐infection model an—important finding that departs from traditional models which assume that reducing the basic reproduction number (R0) below one ensures disease eradication. Our analysis reveals that co‐infection introduces nonlinear dynamics that make disease control more challenging, necessitating more nuanced and aggressive intervention strategies. Additionally, a sensitivity analysis pinpoints the most influential parameters driving transmission, such as contact rates and treatment effectiveness, providing valuable insights for public health decision‐making. It was concluded that malaria‐tuberculosis co‐infection requires integrated control strategies that account for their interactions rather than addressing each disease in isolation. The study offers a robust mathematical framework that not only advances theoretical understanding but also supports evidence‐based policymaking in the fight against these deadly diseases. |
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| ISSN: | 2577-8196 |