Kuan Yang, Ermei Wang, Yinggao Zhou and Kai Zhou
The purpose of this paper is to use analytical method and optimization tools to suggest time-optimal vaccination program for a basic SIR epidemic model with mass action contact…
Abstract
Purpose
The purpose of this paper is to use analytical method and optimization tools to suggest time-optimal vaccination program for a basic SIR epidemic model with mass action contact rate when supply is limited.
Design/methodology/approach
The Lagrange Multiplier Method and Pontryagin’s Maximum Principle are used to explore optimal control strategy and obtain analytical solution for the control system to minimize the total cost of disease with boundary constraint. The numerical simulation is done with Matlab using the sequential linear programming method to illustrate the impact of parameters.
Findings
The result highlighted that the optimal control strategy is Bang-Bang control – to vaccinate with maximal effort until either all of the resources are used up or epidemic is over, and the optimal strategies and total cost of vaccination are usually dependent on whether there is any constraint of resource, however, the optimal strategy is independent on the relative cost of vaccination when the supply is limited.
Practical implications
The research indicate a practical view that the enhancement of daily vaccination rate is critical to make effective initiatives to prevent epidemic from out breaking and reduce the costs of control.
Originality/value
The analysis of the time-optimal application of outbreak control is of clear practical value and the introducing of resource constraint in epidemic control is of realistic sense, these are beneficial for epidemiologists and public health officials.
Details
Keywords
Moontaha Farin, Jarin Tasnim Maisha, Ian Gibson and M. Tarik Arafat
Additive manufacturing (AM), also known as three-dimensional (3D) printing technology, has been used in the health-care industry for over two decades. It is in high demand in the…
Abstract
Purpose
Additive manufacturing (AM), also known as three-dimensional (3D) printing technology, has been used in the health-care industry for over two decades. It is in high demand in the health-care industry due to its strength to manufacture custom-designed and personalized 3D constructs. Recently, AM technologies are being explored to develop personalized drug delivery systems, such as personalized oral dosages, implants and others due to their potential to design and develop systems with complex geometry and programmed controlled release profile. Furthermore, in 2015, the US Food and Drug Administration approved the first AM medication, Spritam® (Apprecia Pharmaceuticals) which has led to tremendous interest in exploring this technology as a bespoke solution for patient-specific drug delivery systems. The purpose of this study is to provide a comprehensive overview of AM technologies applied to the development of personalized drug delivery systems, including an analysis of the commercial status of AM based drugs and delivery devices.
Design/methodology/approach
This review paper provides a detailed understanding of how AM technologies are used to develop personalized drug delivery systems. Different AM technologies and how these technologies can be chosen for a specific drug delivery system are discussed. Different types of materials used to manufacture personalized drug delivery systems are also discussed here. Furthermore, recent preclinical and clinical trials are discussed. The challenges and future perceptions of personalized medicine and the clinical use of these systems are also discussed.
Findings
Substantial works are ongoing to develop personalized medicine using AM technologies. Understanding the regulatory requirements is needed to establish this area as a point-of-care solution for patients. Furthermore, scientists, engineers and regulatory agencies need to work closely to successfully translate the research efforts to clinics.
Originality/value
This review paper highlights the recent efforts of AM-based technologies in the field of personalized drug delivery systems with an insight into the possible future direction.