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Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization.

ABSTRACT: For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.

SUBMITTER: Miura F

PROVIDER: S-EPMC8699630 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

Project description:ImportanceAs COVID-19 vaccine distribution continues, policy makers are struggling to decide which groups should be prioritized for vaccination.ObjectiveTo assess US adults' preferences regarding COVID-19 vaccine prioritization.Design, setting, and participantsThis survey study involved 2 independent, online surveys of US adults aged 18 years and older, 1 conducted by Gallup from September 14 to 27, 2020, and the other conducted by the COVID Collaborative from September 19 to 25, 2020. Samples were weighted to reflect sociodemographic characteristics of the US population.ExposuresRespondents were asked to prioritize groups for COVID-19 vaccine and to rank their prioritization considerations.Main outcomes and measuresThe study assessed prioritization preferences and agreement with the National Academies of Science, Engineering, and Medicine's Preliminary Framework for Equitable Allocation of COVID-19 Vaccine.ResultsA total of 4735 individuals participated, 2730 (1474 men [54.1%]; mean [SD] age, 59.2 [14.5] years) in the Gallup survey and 2005 (944 men [47.1%]; 203 participants [21.5%] aged 55-59 years) in the COVID Collaborative survey. In both the Gallup COVID-19 Panel and COVID Collaborative surveys, respondents listed health care workers (Gallup, 93.6% [95% CI, 91.2%-95.3%]; COVID Collaborative, 80.0% [95% CI, 78.0%-81.9%]) and adults of any age with serious comorbid conditions (Gallup, 78.6% [95% CI, 75.2%-81.7%]; COVID Collaborative, 72.9% [95% CI, 70.7%-74.9%]) among their 4 highest priority groups. Respondents of all political affiliations agreed with prioritizing Black, Hispanic, Native American, and other communities that have been disproportionately affected by COVID-19 (Gallup, 74.2% [95% CI, 70.6%-77.5%]; COVID Collaborative, 84.9% [95% CI, 83.1%-86.5%]), and COVID Collaborative respondents were willing to be preceded in line by teachers and childcare workers (92.5%; 95% CI, 91.2%-93.7%) and grocery workers (85.9%; 95% CI, 84.2%-87.5%). Older respondents in both surveys were significantly less likely than younger respondents to prioritize healthy adults aged 65 years and older among their 4 highest priority groups (Gallup, 23.7% vs 39.1% [χ2 = 2160.8; P < .001]; COVID Collaborative, 23.3% vs 28.8% [χ2 = 5.0198; P = .03]). COVID Collaborative respondents believed the 4 most important considerations for prioritization were preventing COVID-19 spread (78.4% [95% CI, 76.3%-80.3%]), preventing the most deaths (72.1% [95% CI, 69.9%-74.2%]), preventing long-term complications (68.9% [66.6%-71.9%]), and protecting frontline workers (63.8% [95% CI, 61.5%-66.1%]).Conclusions and relevanceUS adults broadly agreed with the National Academies of Science, Engineering, and Medicine's prioritization framework. Respondents endorsed prioritizing racial/ethnic communities that are disproportionately affected by COVID-19, and older respondents were significantly less likely than younger respondents to endorse prioritizing healthy people older than 65 years. This provides reason for caution about COVID-19 vaccine distribution plans that prioritize healthy adults older than a cutoff age without including those younger than that age with preexisting conditions, that aim solely to prevent the most deaths, or that give no priority to frontline workers or disproportionately affected communities.

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Optimal vaccine allocation for COVID-19 in the Netherlands: A data-driven prioritization.

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