Optimizing Vaccine Allocation Strategies in Pandemic Outbreaks: An Optimal Control Approach

Tonkens, Sander; Klaver, Paul De; Salazar, Mauro

doi:10.23919/ecc55457.2022.9838180

Cited by 2 publications

(4 citation statements)

References 21 publications

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“…[27], it is noted that different groups would have different acceptance rates for vaccines. To prevent wasting vaccines, the proportion of allocated vaccines (𝑉 𝑖 ) to group 𝑖 should not exceed the percentage of people who are willing to get vaccinated: 0 ≤ 𝑉 𝑖 ≤ 𝑆 𝑖 (0) * 𝜂 𝑖 (19) where 𝜂 𝑖 is the average vaccine acceptance of group 𝑖, and 𝑆 𝑖 (0) equals the initial population percentage of group 𝑖.…”

Section: 𝑆 ̅ 𝑖 (𝑡mentioning

confidence: 99%

“…The above optimization objective is subject to constraints including (1)- (19), and the obtained optimization problem is a nonlinear programming problem to which a globally optimal solution would be difficult to find. Therefore, linearization techniques will be applied to derive a linear programming problem in the case study.…”

Section: 𝑆 ̅ 𝑖 (𝑡mentioning

confidence: 99%

“…[22] using New York State's COVID-19 data: 𝛽 11 = 0.403, 𝛽 12 = 0.071, 𝛽 21 = 0.154, 𝛽 22 = 0.613 𝐼 1 (0) = 0.84%, 𝐼 2 (0) = 0.16%; 𝑅 1 (0) = 1.68%, 𝑅 2 (0) = 0.32%, 𝐷 1 (0) = 0.084%, 𝐷 2 (0) = 0.016%, 𝑆 1 (0) = 82.3%, 𝑆 2 (0) = 15.6% 𝐵 1 (0) = 𝐵 2 (0) = 0, 𝛾 1 = 0.079, 𝛾 2 = 0.064, 𝜇 1 = 0.00012, 𝜇 2 = 0.00460 Now consider the optimization objective in (19). To minimize 𝐹(𝑉), we need to calculate the two summation terms in (19). Let us first calculate ∑ ∑ 𝐵 𝑖 (𝑡) , which is much more complicated and is a nonlinear function of 𝑉 .…”

Section: Case Studymentioning

confidence: 99%

“…Ref. [19] uses a Susceptible-Exposed-Infected-Recovered (SEIR) model capturing partially and fully vaccinated individuals and considers immunity and infection levels and disease severity specific to each age group to allocate vaccines optimally, allowing for the prioritization of different societal objectives such as minimization of deaths. The SEIR model is also applied in [20] for the vaccine allocation while minimizing total infections and total severe cases.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Zhang¹

2023

IEEE Access

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Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates-the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people*week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people*week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%~2.75% people*week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people*week infections and avoid vaccine waste when more vaccines are available.

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Section: 𝑆 ̅ 𝑖 (𝑡mentioning

confidence: 99%

Section: 𝑆 ̅ 𝑖 (𝑡mentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Zhang¹

2023

IEEE Access

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Data-driven Targeting of COVID-19 Vaccination Programs: An Analysis of the Evidence on Impact, Implementation, Ethics and Equity

McLaren

2023

Preprint

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The data-driven targeting of COVID-19 vaccination programs is a major determinant of the ongoing toll of COVID-19. Targeting of access to, outreach about and incentives for vaccination can reduce total deaths by 20-50 percent relative to a first-come-first-served allocation. This piece performs a systematic review of the modeling literature on the relative benefits of targeting different groups for vaccination and evaluates the broader scholarly evidence -- including analyses of real-world challenges around implementation, equity, and other ethical considerations -- to guide vaccination targeting strategies. Three-quarters of the modeling studies reviewed concluded that the most effective way to save lives, reduce hospitalizations and mitigate the ongoing toll of COVID-19 is to target vaccination program resources to high-risk people directly rather than reducing transmission by targeting low-risk people. There is compelling evidence that defining vulnerability based on a combination of age, occupation, underlying medical conditions and geographic location is more effective than targeting based on age alone. Incorporating measures of economic vulnerability into the prioritization scheme not only reduces mortality but also improves equity. The data-driven targeting of COVID-19 vaccination program resources benefits everyone by efficiently mitigating the worst effects of the pandemic until the threat of COVID-19 has passed.

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Optimizing Vaccine Allocation Strategies in Pandemic Outbreaks: An Optimal Control Approach

Cited by 2 publications

References 21 publications

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Data-driven Targeting of COVID-19 Vaccination Programs: An Analysis of the Evidence on Impact, Implementation, Ethics and Equity

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