Modeling co-dynamics of pneumonia and meningitis diseases

Tilahun, Getachew Teshome

doi:10.1186/s13662-019-2087-3

Cited by 26 publications

(20 citation statements)

References 10 publications

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“…In 2019, Kosasih et al explained the main cause of pneumonia affecting children in early childhood in poor regions of the world [ 8 ]. In 2019, Tilahun et al analysed a coinfection mathematical model for pneumonia and bacterial meningitis [ 16 ]. Mbabazi et al, in 2019, proposed a pneumococcal pneumonia model with time delays mathematically, Hopf-bifurcation was analysed [ 17 ].…”

Section: Literature Surveymentioning

confidence: 99%

Modeling the transmission dynamics of delayed pneumonia-like diseases with a sensitivity of parameters

et al. 2021

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Pneumonia is a highly transmitted disease in children. According to the World Health Organization (WHO), the most affected regions include South Asia and sub-Saharan Africa. 15% deaths of children are due to pneumonia. In 2017, 0.88 million children were killed under the age of five years. An analysis of pneumonia disease is performed with the help of a delayed mathematical modelling technique. The epidemiological system contemplates subpopulations of susceptible, carriers, infected and recovered individuals, along with nonlinear interactions between the members of those subpopulations. The positivity and the boundedness of the ongoing problem for nonnegative initial data are thoroughly proved. The system possesses pneumonia-free and pneumonia existing equilibrium points, whose stability is studied rigorously. Moreover, the numerical simulations confirm the validity of these theoretical results.

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Section: Literature Surveymentioning

confidence: 99%

Modeling the transmission dynamics of delayed pneumonia-like diseases with a sensitivity of parameters

et al. 2021

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“…Pneumonia is one of airborne infectious disease caused by caused by bacteria, viruses, fungi, or parasites which attacks the human lungs or alveoli [9][10][11]. Most of the time pneumonia affects older adults, babies, and people with other diseases or impaired immune systems worldwide.…”

Section: Introductionmentioning

confidence: 99%

HIV/AIDS-Pneumonia Coinfection Model with Treatment at Each Infection Stage: Mathematical Analysis and Numerical Simulation

Teklu

Mekonnen

2021

Journal of Applied Mathematics

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In the paper, we have considered a nonlinear compartmental mathematical model that assesses the effect of treatment on the dynamics of HIV/AIDS and pneumonia coinfection in a human population at different infection stages. Our model revealed that the disease-free equilibrium points of the HIV/AIDS and pneumonia submodels are both locally and globally asymptotically stable whenever the associated basic reproduction numbers ( R H and R P ) are less than unity. Both the submodel endemic equilibrium points are locally and globally asymptotically stable whenever the associated basic reproduction numbers ( R P and R H ) are greater than unity. The full HIV/AIDS-pneumonia coinfection model has both locally and globally asymptotically stable disease-free equilibrium points whenever the basic reproduction number of the coinfection model R H P is less than unity. Using standard values of parameters collected from different kinds of literature, we found that the numerical values of the basic reproduction numbers of the HIV/AIDS-only submodel and pneumonia-only submodel are 17 and 7, respectively, and the basic reproduction number of the HIV/AIDS-pneumonia coinfection model is max 7 , 17 = 17 . Applying sensitive analysis, we identified the most influential parameters to change the behavior of the solution of the considered coinfection dynamical system are the HIV/AIDS and pneumonia transmission rates β 1 and β 2 , respectively. The coinfection model was numerically simulated to investigate the stability of the coinfection endemic equilibrium point, the impacts of transmission rates, and treatment strategies for HIV/AIDS-only, pneumonia-only, and HIV/AIDS-pneumonia coinfected individuals. Finally, we observed that numerical simulations indicate that treatment against infection at every stage lowers the rate of infection or disease prevalence.

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“…These models are usually written in terms of ordinary differential equations. For instance, there are works about the interaction between dengue and leptospirosis ( Alemneh, 2020 ), pneumonia and meningitis ( Tilahun, 2019 ), HIV and hepatitis C ( Moualeu et al., 2011 ), two strains of influenza ( Zhang et al., 2013 ), two strains of dengue ( Anggriani et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

The co-circulation of two infectious diseases and the impact of vaccination against one of them

Puga

Monteiro

2021

Ecological Complexity

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Modeling co-dynamics of pneumonia and meningitis diseases

Cited by 26 publications

References 10 publications

Modeling the transmission dynamics of delayed pneumonia-like diseases with a sensitivity of parameters

Modeling the transmission dynamics of delayed pneumonia-like diseases with a sensitivity of parameters

HIV/AIDS-Pneumonia Coinfection Model with Treatment at Each Infection Stage: Mathematical Analysis and Numerical Simulation

The co-circulation of two infectious diseases and the impact of vaccination against one of them

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