An optimal control model for COVID‐19, zika, dengue, and chikungunya co‐dynamics with reinfection

Omame, Andrew; Isah, Mary Ele; Abbas, Mujahid

doi:10.1002/oca.2936

Cited by 30 publications

(8 citation statements)

References 41 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A novel mathematical model has been created and analyzed to evaluate the interplay between COVID-19, Zika, chikungunya, and dengue co-dynamics. The aim is to determine the influence of COVID-19 on the dynamics of Zika, dengue, and chikungunya, as well as the reverse effect [2]. A proposed SEQAIHR model with saturated treatment is presented, and the biological feasibility of the model solutions is assessed, along with the calculation of the basic reproduction number (R0).…”

Section: Introductionmentioning

confidence: 99%

Mathematical Analysis of Fractal-Fractional Mathematical Model of COVID-19

Sinan

Alharthi

2023

Fractal Fract

View full text Add to dashboard Cite

In this work, we modified a dynamical system that addresses COVID-19 infection under a fractal-fractional-order derivative. The model investigates the psychological effects of the disease on humans. We establish global and local stability results for the model under the aforementioned derivative. Additionally, we compute the fundamental reproduction number, which helps predict the transmission of the disease in the community. Using the Carlos Castillo-Chavez method, we derive some adequate results about the bifurcation analysis of the proposed model. We also investigate sensitivity analysis to the given model using the criteria of Chitnis and his co-authors. Furthermore, we formulate the characterization of optimal control strategies by utilizing Pontryagin’s maximum principle. We simulate the model for different fractal-fractional orders subject to various parameter values using Adam Bashforth’s numerical method. All numerical findings are presented graphically.

show abstract

Section: Introductionmentioning

confidence: 99%

Mathematical Analysis of Fractal-Fractional Mathematical Model of COVID-19

Sinan

Alharthi

2023

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…In relation to COVID-19, several studies have been conducted on the co-dynamics of the virus with other diseases. The authors in 24 – 30 proposed and analyzed a deterministic model for COVID-19 co-infection. In 24 , a compartmental model of cholera-COVID-19 co-infection was investigated by considering the transmission dynamics of both diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Through numerical simulations, the studies show the substantial impact of SARS-CoV-2 prevention in decreasing the burden of co-infections with Hepatitis B and Zika and vice versa. To assess the impacts of COVID-19 on the dynamics of Zika, Dengue, and Chikungunya as well as the reverse, a mathematical model for COVID-19, Zika, Chikungunya, and dengue co-dynamics is developed and explored in 30 . In this paper, for the purpose of preventing the co-circulation of the diseases, time-dependent controls are taken into account and assessed utilizing Pontryagin’s principle.…”

Section: Introductionmentioning

confidence: 99%

Co-infection dynamics of COVID-19 and HIV/AIDS

Batu,

Obsu,

Deressa

2023

Sci Rep

View full text Add to dashboard Cite

Although there are many results that can be used to treat and prevent Coronavirus Disease 2019 (COVID-19) and Human Immunodeficiency Virus (HIV), these diseases continue to be public health concerns and cause socioeconomic consequences. Following compromised immunity, COVID-19 is considered to be a challenge for people with HIV. People with advanced HIV are considered a vulnerable population at high risk in several case studies that discuss COVID-19 and HIV co-infection. As there is no cure for HIV and there is a chance of contracting COVID-19 again, co-infection continues to pose a problem. The purpose of this study is to investigate the impact of intervention strategies and identify the role of different parameters in risking people living with HIV to death when they get infected with COVID-19. This is achieved through the development and rigorous analysis of a mathematical model that considers a population at risk of death due to COVID-19 and HIV. The model formulation provides a detailed explanation of the transmission dynamics of COVID-19 and HIV co-infection. The solution’s invariant region, positivity, and boundedness were established. The reproduction numbers of the sub-models and the co-infection model were determined. The existence and stability of equilibria, including backward bifurcation for the COVID-19 sub-model, were examined. The epidemiological significance of backward bifurcation is that the condition $${\mathscr {R}}_0$$ R 0 less than 1 for eliminating COVID-19, though necessary, is no longer sufficient. Parametric estimation and curve fitting were performed based on data from Ethiopia. Numerical simulations were employed to support and clarify the analytical findings and to show some parameter effects on COVID-19 and HIV co-infection. Accordingly, the simulations indicated that parameters $$\gamma _c$$ γ c , $$\gamma _h$$ γ h , $$\epsilon$$ ϵ , and $$\kappa$$ κ , related to HIV patients’ exposure to other diseases and the increase in infectiousness, have a positive role in increasing the number of co-infections. On the other hand, an increase in COVID-19 vaccination ($$\xi$$ ξ ) shows the suppression of co-infection cases. In addition, treating co-infected individuals for COVID-19, increasing treatment rates $$\alpha$$ α and $$\varphi$$ φ , reduces the death risk of HIV-infected individuals due to the co-infection burden. It was implied that improving vaccine delivery programs and other medical interventions have important contributions to lowering the risk of COVID-19 infection-related fatalities in HIV patients.

show abstract

“…Recently, many researchers analyzed their COVID-19 model with backward [16] , forward–backward [17] , [18] , transcritical [19] , Hopf [20] bifurcations. With their model, they used optimal control variables to reduce COVID-19 [21] , [22] such as treatment, infection prevention, and vaccination.…”

Section: Introductionmentioning

confidence: 99%