Receding Horizon Control of HIV

David, J. W.; Tran, Hien T.; Banks, Harvey Thomas

doi:10.1002/oca.969

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…MPC has had some use within the epidemiological literature, the majority being for control of drug applications for single individuals rather than control of epidemics at the population level. Examples include finding management strategies for HIV that are robust to measurement noise and modelling errors [36,37], and control of insulin delivery in patients with diabetes [38]. These studies highlight the benefits of MPC for robust control, i.e.…”

Section: (B) Model Predictive Controlmentioning

confidence: 99%

Applying optimal control theory to complex epidemiological models to inform real-world disease management

Bussell

Dangerfield

Gilligan

et al. 2019

Phil. Trans. R. Soc. B

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Mathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach—optimal control theory—allows the best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model, we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of humans, animals and plants. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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Section: (B) Model Predictive Controlmentioning

confidence: 99%

Applying optimal control theory to complex epidemiological models to inform real-world disease management

Bussell

Dangerfield

Gilligan

et al. 2019

Phil. Trans. R. Soc. B

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show abstract

“…In another recent study, demonstrate a σ − modif ication based adaptive estimation routine of HIV parameters in presence of mutant cells. David et al (2011) discuss a methodology based on an iterative receding horizon control, kalman filtering and state estimation framework for HIV. presents a multi-stage smoothing-based (MSSB) and the spline-enhanced nonlinear least squares (SNLS) approach, to estimate all HIV viral dynamic parameters.…”

Section: Introduction: Background and Motivationmentioning

confidence: 99%

“…Hartmann et al (2012), in their study, explore nonlinear bayesian filtering through extended kalman filter and particle filter for joint state and time-varying parameter estimation HIV problem. David et al (2011) discuss a receding horizon based methodology for the estimation problem and explore how ideas from stochastic estimation can be used to create a better treatment methodology.…”

Section: Introduction: Background and Motivationmentioning

confidence: 99%

Estimation of CD4+ T Cell Count Parameters in HIV/AIDS Patients Based on Real-time Nonlinear Receding Horizon Control

Sun

Turkoglu

2018

Int. J. Control Autom. Syst.

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An increasing number of control techniques are introduced to HIV infection problem to explore the options of helping clinical testing, optimizing drug treatments and to study the drug resistance situations. In such cases, complete/accurate knowledge of the HIV model and/or parameters is critical not only to monitor the dynamics of the system, but also to adjust the therapy accordingly. In those studies, existence of any type of unknown parameters imposes severe set-backs and becomes problematic for the treatment of the patients. In this work, we develop a real-time adaptive nonlinear receding horizon control approach to aid such scenarios and to estimate unknown constant/time-varying parameters of nonlinear HIV system models. For this purpose, the problem of estimation is updated by a series of finite-time optimization problem which can be solved by backwards sweep Riccati method in real time without employing any iteration techniques. The simulation results demonstrates the fact that proposed algorithm is able to estimate unknown constant/time-varying parameters of HIV/AIDS model effectively and provide a unique, adaptive solution methodology to an important open problem.

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“…These treatment methods have been studied in detail, though a lack of double-blind, randomized clinical trials has lead to a lack of consensus on the effectiveness of these types of treatments [22] [23] [24] [25]. We implement the optimal STI using the receding horizon control (RHC) methodology [12]. Since RHC is a feedback control, it allows for adjustment for unexpected perturbations to the treatment schedule, e.g.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Control Approach to Structured Treatment Interruptions for HIV Patients: A Personalized Medicine Perspective

Attarian¹,

Tran²

2017

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Highly Active Antiretroviral Therapy (HAART) has changed the course of human immunodeficiency virus (HIV) treatments since its introduction. However, for many patients, long term continuous HAART is expensive and can include problems with drug toxicity and side effects, as well as increased drug resistance. Because of these reasons, some HIV infected patients will voluntarily terminate HAART. Some of these patients will also interrupt the continuous prescribed therapies for short or long periods. After discontinuing HAART, patients will usually experience a rapid increase in viral load coupled with an immediate decline in CD4+ counts. The canonical example of a patient undergoing unsupervised breaks in HAART is that of the "Berlin patient". In this case, the patient was able to control viral load in the absence of treatment by cycling HAART on and off due to non-related infections. Due to this patient, interest in the use of structured treatment interruptions (STI) as a mechanism to regulate an HIV infection piqued. This paper describes an optimal control approach to determine STI regimen for HIV patients. The optimal STI was implemented in the context of the receding horizon control (RHC) using a mathematical model for the in-vivo dynamics of an HIV type 1 infection. Using available clinical data, we calibrate the model by estimating on a patient specific basis, a best estimable set of parameters using sensitivity analysis and subset selection. We demonstrate how customized STI protocols can be designed through the variation of control parameters on a patient specific basis.

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Receding Horizon Control of HIV

Cited by 15 publications

References 23 publications

Applying optimal control theory to complex epidemiological models to inform real-world disease management

Applying optimal control theory to complex epidemiological models to inform real-world disease management

Estimation of CD4+ T Cell Count Parameters in HIV/AIDS Patients Based on Real-time Nonlinear Receding Horizon Control

An Optimal Control Approach to Structured Treatment Interruptions for HIV Patients: A Personalized Medicine Perspective

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