Vaccine optimization for COVID-19: Who to vaccinate first?

Abstract

Vaccines, when available, will likely become our best tool to control the COVID-19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age-stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four different metrics (deaths, symptomatic infections, and maximum non-ICU and ICU hospitalizations) under many scenarios. We find that a vaccine with effectiveness ≥50% would be enough to substantially mitigate the ongoing pandemic, provided that a high percentage of the population is optimally vaccinated. When minimizing deaths, we find that for low vaccine effectiveness, irrespective of vaccination coverage, it is optimal to allocate vaccine to high-risk (older) age groups first. In contrast, for higher vaccine effectiveness, there is a switch to allocate vaccine to high-transmission (younger) age groups first for high vaccination coverage. While there are other societal and ethical considerations, this work can provide an evidence-based rationale for vaccine prioritization.

Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality

“…Unlike prior deterministic compartmental models of SARS-CoV-2 transmission and COVID-19 vaccination, 8 , 9 , 10 , 48 , 49 , 50 our agent-based simulation model captured heterogeneous mixing between individuals and within households, schools, workplaces, and communities and allowed for joint consideration of various interventions. In this study, we did not intend to forecast COVID-19 burden for a specific population but to support public health officials by comparing the potential outcomes of vaccination and NPI scenarios to, for example, inform the public on the potential effects of vaccine uptake as well as continued adherence to existing mitigation strategies.…”

“… 8 Recent mathematical modeling suggests that prioritizing vaccine distribution and uptake would maximize the benefit of a highly efficacious vaccine. 8 , 9 , 10 Bartsch et al 9 estimated that at least 75% of the US population would need to be vaccinated with an efficacy of 70% to reduce the epidemic peak by more than 99% without other interventions. Considering the complexities of large-scale vaccine production, distribution, and uptake, achieving high coverage will be challenging.…”

Reopening California : Seeking Robust, Non-Dominated COVID-19 Exit Strategies

“…While seeking robust, non-dominated policies, decision-makers have to consider a number of factors, including the goals that one seeks to achieve, the policy levers that are within reach, uncertainties that can influence the decision between those policy levers, and how those elements are Some rigorous analyses considered robustness and pareto-efficiency of COVID-19 policies. For example, analyses evaluating vaccination strategies [15,16] do account for uncertainties and multiple goals.…”

A Net Benefit Approach for the Optimal Allocation of a COVID-19 Vaccine

“…Health economic models of the effectiveness and cost effectiveness of different combinations of NPIs and vaccines can enable more informed vaccine allocation decisions, by capturing the dynamic relationship between vaccine characteristics, allocation strategies, NPIs and population behaviours. Whilst hundreds of models have been developed internationally to support decision making and public health messaging, only a handful of evaluations of vaccine allocation and prioritisation strategies have been published to date [ 13 , 15 – 18 ]. Only one of the studies evaluates scenarios with concurrent public health mitigation strategies [ 13 ].…”