How the Interval Between Primary and Booster Vaccination Affects Long-Term Disease Dynamics

Shoukat, Affan; Espíndola, Aquino L.; Röst, Gergely; Moghadas, Seyed M.

doi:10.1142/9789813227880_0004

Cited by 3 publications

(3 citation statements)

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“…In conclusion, as shown in our global analysis of the system, the delay term (τ ) plays an important role in determining the possibility of disease control, which is reflected in the value of the effective reproduction number (R c ). As also demonstrated in our previous studies [9,18], the delay term can be regarded as a control parameter in epidemic models. Apart from the theoretical aspects of the global dynamics, this control parameter could provide important information on the long-term disease dynamics; for example to minimize endemic states of drug-resistance with delay in start of treatment [9], or to reduce timelines for achieving disease control in vaccine-preventable diseases with optimal booster vaccine schedule [18].…”

Section: Discussionsupporting

confidence: 56%

“…Although such delay in start of treatment could play a critical role in disease dynamics, current treatment practices largely pivot towards management of infection and severe outcomes in patients, leaving out the possible evolution and epidemiological consequences of resistance spread under the selection pressure of drugs. The impact of delay as a control parameter has also been recognized in vaccine-preventable diseases for scheduling booster doses in order to determine the optimal timing of vaccination for generating a longer term herd protection and reducing the impact of disease on the population [18]. Collectively, these studies suggest that the 'delay' term in epidemic models could reveal important factors that influence disease dynamics, and pertinent strategies for control and prevention.…”

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confidence: 99%

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Global dynamics of an epidemiological model with age-of-infection dependent treatment rate

et al. 2018

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We revisit a previously established model for influenza transmission dynamics, in which antiviral treatment as a single containment strategy was administered within a specified window of opportunity for initiating treatment. We extend this model to a more general framework with age-of-infection dependent treatment rates. The resulting age structured model can be transformed into a closed system of delay differential equations, for which we perform a complete global stability analysis. By constructing suitable Lyapunov functions, we show that the effective reproduction number fully characterizes the possible outcomes of disease dynamics. Our results allow us to evaluate treatment strategies and examine the impact of treatment delays on the potential success of disease control.

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Section: Discussionsupporting

confidence: 56%

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Global dynamics of an epidemiological model with age-of-infection dependent treatment rate

et al. 2018

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“…Some vaccines are a very limited and costly resource, so some research has focused on the optimization of the distribution among the population (considering, for example, age, gender, location, etc.) (HARVEY et al, 2016;SHOUKAT et al, 2016;GIERSING et al, 2017;KUROSKY;DAVIS;KRISHNARAJAH, 2016;HARDT et al, 2016).…”

Section: Vaccine Supply Chainmentioning

confidence: 99%

Stochastic optimization and machine learning applied in the demand forecast, allocation and distribution of vaccines between Brazilian states.

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Optimization methods for large-scale vaccine supply chains: a rapid review

et al. 2022

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Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.

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How the Interval Between Primary and Booster Vaccination Affects Long-Term Disease Dynamics

Cited by 3 publications

References 12 publications

Global dynamics of an epidemiological model with age-of-infection dependent treatment rate

Global dynamics of an epidemiological model with age-of-infection dependent treatment rate

Stochastic optimization and machine learning applied in the demand forecast, allocation and distribution of vaccines between Brazilian states.

Optimization methods for large-scale vaccine supply chains: a rapid review

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