A Stochastic Model for the Immune Response to HIV/AIDS-TB Pathogenesis and Applications on Young Adults.

Author:

Shoko Claris1,Molefe Wilford1,Nadarajah Saralees2

Affiliation:

1. University of Botswana

2. University of Manchester

Abstract

Abstract Background Coinfection of Mycobacterium tuberculosis (M.TB) and human immunodeficiency virus (HIV) accelerates immune deterioration. Approximately one in three people living with HIV dies due to TB. This is likely to hinder progress towards the achievement of the 2030 Sustainable Development Goal of ending the HIV and TB pandemic.Methods In this paper, we model HIV-TB interaction within the host using the deterministic approach. Further analysis of the progression of HIV in patients who were enrolled with TB and patients who developed TB during treatment is done using multi-state modelling. This study is done on HIV-TB co-infected young adults (15 to 34 years) from South Africa. HIV progression for this cohort is divided into 4 states (state 1: Undetectable viral load below 50 HIV RNA per mL; state 2: HIV RNA ranging from 50 to below 10 000 copies/mL; state 3: at least 10 000 HIV RNA copies/mL); and state 4: Death).Results Results from the analysis show that TB increases the odds of an unsuppressed viral load. This is quite notable for patients in state 2 where the log-linear effect of having TB at enrolment is approximately − 8.7 for the transition to state 1 and − 0.64 for the transition to state 3. At state 2 of HIV progression, the rate of virologic failure is also very high and most deaths are observed from this state.Conclusion This calls for the need to closely monitor HIV patients for any possibility of TB coinfection. This can be done by assigning treatment partners to all HIV patients.

Publisher

Research Square Platform LLC

Reference20 articles.

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2. HIV TB coinfection - perspectives from India;Rewari Bharat Bhushan;Expert Review of Respiratory Medicine,2021

3. CD4 and CD8 T Cell Immune Activation during Chronic HIV Infection: Roles of Homeostasis, HIV, Type I IFN, and IL-7; \emph{J Immunol 2011};Catalfamo M

4. Christopher Jackson. Multi-State Markov and Hidden Markov Models in Continuous Time. 2023. Version 1.7.1. L https://github.com/chjackson/msm, https://chjackson.github.io/msm/

5. Modeling of HIV/AIDS dynamic evolution using non-homogeneous semi-markov process;Dessie ZG;Springerplus,2014

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