Transport and optimal control of vaccination dynamics for COVID-19

Zaitri, Mohamed Abdelaziz; Bibi, Mohand Ouamer; Torres, Delfim F. M.

doi:10.1016/b978-0-32-390504-6.00007-3

Cited by 3 publications

(6 citation statements)

References 23 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2023); Shen et al (2021); Zaitri et al (2022). In particular, some works have combined optimal control with age-structured models to find the optimal vaccination allocation Avcı and Yurtoglu (2023); Chhetri et al (2022); Kumar et al (2021).…”

Section: Related Studies That Employ Optimal Control Methodsmentioning

confidence: 99%

“…There exist numerous other works that use classical optimal control to identify optimal COVID-19 vaccination strategies, most of them minimizing an objective functional which accounts for infected cases, deaths or the number of vaccines Agossou et al (2021); Al-arydah (2023); Salcedo-Varela et al (2023); Shen et al (2021); Zaitri et al (2022). In particular, some works have combined optimal control with age-structured models to find the optimal vaccination allocation Avcı and Yurtŏglu (2023); Chhetri et al (2022); Kumar et al (2021).…”

Section: Related Studies That Employ Optimal Control Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

Gonzalez-Parra,

Mahmud,

Kadelka

2024

Preprint

View full text Add to dashboard Cite

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

show abstract

Section: Related Studies That Employ Optimal Control Methodsmentioning

confidence: 99%

Section: Related Studies That Employ Optimal Control Methodsmentioning

confidence: 99%

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

Gonzalez-Parra,

Mahmud,

Kadelka

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The use of facemasks, hand sanitizers, and social distancing as control variables, as mentioned in Refs. [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Care of COVID-19 patients, active screens, and testing as control variables, as mentioned in Refs.…”

Section: Introductionmentioning

confidence: 99%

“…The recovery rate of asymptomatic infected individuals in treatment per unit time at time t is the control variable, as mentioned in Refs. [18,19,21,22,24]. The recovery rate of symptomatic infected individuals in treatment per unit time at time t is the control variable, as mentioned in Refs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity Analysis and Optimal Control of Covid 19 Model

Firmansyah

Rangkuti

2023

Jambura J. Biomath.

View full text Add to dashboard Cite

Coronavirus infection is a disease that causes death and threatens human life; for prevention, it is necessary to quarantine susceptible, exposed, and infected populations and vaccinate the entire population. This kind of quarantine and vaccination is intended to reduce the spread of coronavirus. Epidemiological models are a strategy used by public health practitioners to prevent and fight diseases. However, to be used in decision making, mathematical models must be carefully parameterized and validated using epidemiological and entomological data. Epidemiological models: susceptible, symptomatic, contagious, and recovering. In this study, sensitivity analysis and optimal control were performed to determine the relative importance of the model parameters and to minimize the number of infected populations and control measures against the spread of the disease. Sensitivity analysis was carried out using a sensitivity index to measure the relative change in the basic reproduction number for each parameter, and this control function was applied to the dynamic modeling of the spread of COVID-19 using the Pontryagin Minimum Principle. We will describe the formulation of a dynamic system for the spread of COVID-19 with optimal control and then use Pontryagin’s Minimum Principle to find optimal control solutions. In this article, COVID-19 cases in the USA and India serve as examples of the efficiency of control measures. The results obtained revealed that the parameters that became the basis for reducing the number of infected with COVID-19 for the two countries, the USA and India, are effective transmission rates from S to E, (β), transmission rates from E to I, (α), and transmission rates from S to R, (ps), which are the main parameters to watch for growth with respect to Basic Reproduction rates (R0). Finally, three controls were simulated in cases I (in the USA) and II (in India) in the interval t ∈ [0, 15]. For all controls, the effectiveness was close to 50% in India and 100% in the USA to reduce the spread of COVID 19. According to the findings, if these three controls were implemented ideally from the start of the pandemic, the number of sufferers.

show abstract