Mathematical modelling of tuberculosis as an opportunistic respiratory co-infection in HIV/AIDS in the presence of protection

Nthiiri, Joyce K.; Lawi, G. O.; Manyonge, Alfred

doi:10.12988/ams.2015.54365

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…But, recently a lot of ground work has been covered in the mathematical modelling of coinfection of different pathogens though very little was done in the modelling of HIV/TB co infection. David, 2013;Nthiiri, 2015;Roeger et al, (2009), Shah and Jyota (2013) have developed mathematical models of HIV/TB co-infection under TB treatment. Their work did not include anti-HIV treatment.…”

Section: Tuberculosis (Tb) and Human Immunodeficiencymentioning

confidence: 99%

Semi-Analytic Solution of HIV and TB Co-Infection Model

Bolarin¹,

Omatola²,

Aiyesimi³

et al. 2017

Journal of Applied Sciences and Environmental Management

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Section: Tuberculosis (Tb) and Human Immunodeficiencymentioning

confidence: 99%

Semi-Analytic Solution of HIV and TB Co-Infection Model

Bolarin¹,

Omatola²,

Aiyesimi³

et al. 2017

Journal of Applied Sciences and Environmental Management

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“…Djuikem et al [15] proposed a model of the coffee leaf rust (CLR) with optimal control. The co-infection concept has been captured in several mathematical models of infectious diseases; see, for example, HIV-Tuberculosis co-infection ( [16], [17]), malaria and cholera [18], pneumonia and typhoid [19]. Co-infection phenomena, like human diseases, are expected to alter the course of infection in co-infected plants [20].…”

Section: Introductionmentioning

confidence: 99%

An optimal control model for Coffee Berry Disease and Coffee Leaf Rust co-infection

Nyaberi,

MUTUKU,

MALONZA

et al. 2024

J. Math. Anal. Model.

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In the 1980s, coffee production in Kenya was peaking at an average of 1.7 million bags annually. Since then, this production has been declining to the current production of below 0.9 million bags annually. Coffee berry disease (CBD) and Coffee leaf rust (CLR) are some of the causes of this decline. This is due to a lack of sufficient knowledge on optimal control strategies for co-infection of CBD and CLR. In this research, we derive a system of ODEs from the mathematical model for co-infection of CBD and CLR with prevention of CBD infection, prevention of CLR infection, the treatment of CBD-infected coffee plants, the treatment of CLR-infected coffee plants, the treatment of CBD-CLR Co-infected coffee plants, elimination of Colletotrichum kahawae pathogens and elimination of Hemileia vastatrix pathogens to perform optimal control analysis. An optimal control problem is formulated andsolved using Pontryagin’s maximum principle. The outcomes of the model’s numerical simulations indicate that combining all controls would be the best strategy for slowing the spread of the CBD-CLR co-infection.

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“…Limited TB studies have considered models incorporating movement via mass trans-portation [7], or taking into account the impact of sudden blips of immigration, which may be central to TB re-emergence [8,9,10,11,12], or that account for coinfections, specially with HIV [13,14,15,16,17,18], or that account for relapse [6,19,20,21,22], or that account for antibiotic, drug, and ultra-drug resistance [23,24,25,26,27,28], or models that account for TB re-activation and progression [29,30,31]. In addition, models assuming negligible immigration might not capture the real dynamics of tuberculosis in open populations when high levels of diversity is caused by immigrants [24].…”

mentioning

confidence: 99%

The role of mobility and health disparities on the transmission dynamics of Tuberculosis

Moreno

Espinoza

Barley

et al. 2017

Theor Biol Med Model

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BackgroundThe transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.MethodsThis study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.ResultsThe study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.ConclusionsThe study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.

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Mathematical modelling of tuberculosis as an opportunistic respiratory co-infection in HIV/AIDS in the presence of protection

Cited by 4 publications

References 12 publications

Semi-Analytic Solution of HIV and TB Co-Infection Model

Semi-Analytic Solution of HIV and TB Co-Infection Model

An optimal control model for Coffee Berry Disease and Coffee Leaf Rust co-infection

The role of mobility and health disparities on the transmission dynamics of Tuberculosis

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