Optimal control of intervention strategies and cost effectiveness analysis for a Zika virus model

Momoh, Abdulfatai Atte; Fügenschuh, Armin

doi:10.1016/j.orhc.2017.08.004

Cited by 48 publications

(30 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, Agusto and Khan studied the influence averted ratio (IAR) and average cost effectiveness ratio (ACER). And others can be found in Wang et al and Momoh and Fĺźgenschuh …”

Section: Numerical Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal control and stability analysis of an online game addiction model with two stages

Guo

2020

Math Meth Appl Sci

View full text Add to dashboard Cite

In this paper, we establish a mathematical model of online game addiction with two stages to research the dynamic properties of it. The existence of all equilibria is obtained, and the basic reproduction number is calculated by the method of next‐generation matrix. The global asymptotic stability of disease‐free equilibrium (DFE) is proved by comparison principle, and the global asymptotic stability of endemic equilibrium (EE) is proved by constructing an appropriate Lyapunov function. Then we use the Pontryagin's maximum principle to find the optimal solution of the model, so that the number of infected people can be minimized. In numerical simulation, firstly, we validate the global stability of DFE and EE. Secondly, we consider three kind of control measures (treatment, isolation, and education) and divide them into four cases. The models with control and without control are solved numerically by using forward and backward sweep Runge‐Kutta method. In order to achieve the best control effect, we suggest that three kind of measures should be used simultaneously according to the optimal control strategy.

show abstract

“…For example, Agusto and Khan studied the influence averted ratio (IAR) and average cost effectiveness ratio (ACER). And others can be found in Wang et al and Momoh and Fĺźgenschuh …”

Section: Numerical Simulationmentioning

confidence: 99%

“…And others can be found in Wang et al 18 and Momoh and Fĺźgenschuh. 27 In this paper, the authors are more concerned about the number of infected people. On the premise of acceptable cost, we choose the strategy with the least number of infected people as the optimal strategy.…”

Section: The Simulation Of Optimal Controlmentioning

confidence: 99%

Optimal control and stability analysis of an online game addiction model with two stages

Guo

2020

Math Meth Appl Sci

View full text Add to dashboard Cite

show abstract

“…First, the model without optimum control and with the optimum control model are solved using a fourth-order Runge-Kutta scheme with MATLAB software for 50 months. The optimal control set is obtained by solving the optimality system, which consists of the state and adjoint systems with the transversality conditions or boundary conditions [21,[26][27][28]. The weight constant values in the objective functional are chosen to be A 1 = 100, A 2 = 0.03, B 1 = 100, and B 2 = 140.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

Modeling and Optimal Control on the Spread of Hantavirus Infection

2019

View full text Add to dashboard Cite

In this paper, optimal control theory is applied to a system of ordinary differential equations representing a hantavirus infection in rodent and alien populations. The effect of the optimal control in eliminating the rodent population that caused the hantavirus infection is investigated. In addition, Pontryagin’s maximum principle is used to obtain the necessary condition for the controls to be optimal. The Runge–Kutta method is then used to solve the proposed optimal control system. The findings from the optimal control problem suggest that the infection may be eradicated by implementing some controls for a certain period of time. This research concludes that the optimal control mathematical model is an effective method in reducing the number of infectious in a community and environment.

show abstract

“…Mankind has witnessed several pandemics in the past including plague, leprosy, smallpox, tuberculosis, AIDS, cholera, and malaria [1] [2] [3] . The historic timeline of pandemics suggests that the frequency of occurrence is increasing and in an era wherein globalization is happening at an accelerated pace, we are more likely to confront many such threats in the near future [4] [5] [6] [7] .…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning-based decision support system for COVID-19

Padmanabhan

Meskin

Khattab

et al. 2021

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Globally, informed decision on the most effective set of restrictions for the containment of COVID-19 has been the subject of intense debates. There is a significant need for a structured dynamic framework to model and evaluate different intervention scenarios and how they perform under different national characteristics and constraints. This work proposes a novel optimal decision support framework capable of incorporating different interventions to minimize the impact of widely spread respiratory infectious pandemics, including the recent COVID-19, by taking into account the pandemic's characteristics, the healthcare system parameters, and the socio-economic aspects of the community. The theoretical framework underpinning this work involves the use of a reinforcement learning-based agent to derive constrained optimal policies for tuning a closed-loop control model of the disease transmission dynamics.

show abstract

Optimal control of intervention strategies and cost effectiveness analysis for a Zika virus model

Cited by 48 publications

References 24 publications

Optimal control and stability analysis of an online game addiction model with two stages

Optimal control and stability analysis of an online game addiction model with two stages

Modeling and Optimal Control on the Spread of Hantavirus Infection

Reinforcement learning-based decision support system for COVID-19

Contact Info

Product

Resources

About