OBJECTIVES: COVID-19 vaccine boosters are available in many countries. Public health policymakers face difficult choices over which booster brand to recommend, given limited budgets and the need to maximize health gains. Here, we provide a conceptual model to identify the best booster strategies for age-identified subpopulations under different conditions.

METHODS: A constrained optimization model with an objective function to minimize bed-days was developed that varied population proportion receiving different booster options by age, to identify the best booster strategy that minimized bed-days with a constraint of maximum healthcare expenditure of US$2.10/person. It included a 3-month decision-tree model to calculate bed-days, with the following health states: healthy/asymptomatic; mild (not hospitalized); moderate (general ward); severe (intensive care unit [ICU], no mechanical ventilation); critical (requiring mechanical ventilation); and death. Medical resource utilization (MRU) costs and hospital bed-days were calculated for each health state. The base country was Brazil. Three booster options, B1 (US$1), B2 (US$2), and no-booster (NB, US$0) were considered. Based on real-world effectiveness estimates, B1 and B2 were assumed to be 55% and 75% effective against mild/moderate COVID-19, respectively. Both reduced severe/critical COVID-19 by 90%. The target population was adults eligible for boosters, stratified by age.

RESULTS: The best booster strategy identified recommended 100% coverage of those eligible, with B1 for population <70 years and B2 for population ≥70 years. Compared with NB, bed-days were reduced by 75%, hospitalizations by 68%, and ICU admissions by 90% leading to a 60% reduction in total costs (81% reduction in MRU costs). Within individual age-groups, costs were reduced by 57%-66% based on the age-specific disease risk.

CONCLUSIONS: A constrained optimization model identifies the best age-specific booster allocation strategy to minimize hospital bed-days across different age groups without exceeding a predefined budget. Decision-makers could use this method to achieve the best possible health outcomes when healthcare resources are limited.