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

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The COVID-19 pandemic necessitates effective control measures, with vaccines being a primary tool.
• Anticipated vaccine shortages require strategic allocation to maximize public health benefits.
• Mathematical modeling can inform optimal vaccine distribution strategies.

Purpose of the Study:

• To determine optimal vaccine allocation strategies to mitigate the COVID-19 pandemic under various scenarios.
• To evaluate the impact of vaccine effectiveness and coverage on allocation priorities.
• To provide an evidence-based rationale for vaccine prioritization.

Main Methods:

• An age-stratified mathematical model was developed to simulate COVID-19 transmission and outcomes.
• Optimization algorithms were employed to identify vaccine allocation strategies.
• Scenarios considered different vaccine effectiveness levels, vaccination coverages, and prioritization metrics (deaths, symptomatic infections, hospitalizations).

Main Results:

• A vaccine with effectiveness of 50% or higher can substantially mitigate the pandemic if optimally allocated and widely administered.
• For low vaccine effectiveness, prioritizing older, high-risk age groups is optimal for minimizing deaths, regardless of coverage.
• For high vaccine effectiveness and high vaccination coverage, prioritizing younger, high-transmission age groups becomes optimal for minimizing deaths.

Conclusions:

• Optimal vaccine allocation strategies are dynamic and depend on vaccine characteristics (effectiveness) and population behaviors (transmission patterns).
• Prioritizing high-risk groups is crucial for low-effectiveness vaccines, while prioritizing high-transmission groups can be more effective for high-effectiveness vaccines.
• This study provides a quantitative framework to support evidence-based decision-making for vaccine prioritization during pandemics.