Identification of an Optimal COVID-19 Booster Allocation Strategy to Minimize Hospital Bed-Days with a Fixed...

Area of Science:

• Health Economics
• Epidemiology
• Public Health Policy

Background:

• Healthcare decision-makers require cost-effective COVID-19 booster strategies to maximize health gains within budget constraints.
• Limited resources necessitate optimized allocation of vaccine boosters to mitigate disease impact.

Purpose of the Study:

• To develop and apply a constrained optimization model for identifying optimal COVID-19 booster allocation strategies.
• To minimize healthcare resource utilization, specifically bed-days, by varying booster proportions across age groups.

Main Methods:

• A constrained optimization model was developed to evaluate booster allocation strategies.
• Booster options B1 ($1, 55%/75%/90% efficacy) and B2 ($2, 55%/75%/90% efficacy) were compared against no booster (NB, $0).
• The model optimized booster selection stratified by age within a $2.10 per person expenditure limit, using Brazil as a base case.

Main Results:

• The optimal strategy involved allocating booster B1 to individuals under 70 and B2 to those 70 and older to minimize bed-days.
• This strategy reduced bed-days by 75%, hospital admissions by 68%, and ICU admissions by 90% compared to no booster.
• Total costs decreased by 60%, with an 81% reduction in overall medical resource use.

Conclusions:

• The constrained optimization model provides a framework for effective vaccine booster allocation, balancing cost and healthcare outcomes.
• Targeted booster strategies, considering age and efficacy, can significantly enhance public health responses to COVID-19.
• This approach can guide healthcare decision-makers in resource-limited settings to achieve maximal health benefits.