In December 2020, with a limited number of vaccines available, the CDC had to make a hard decision: Who gets the COVID-19 vaccines first? It decided to divide the U.S. population into four groups for vaccine prioritization based on age, occupation, living condition and known COVID-19 risk factors.
Using a new model and an Iowa State University supercomputer, we compared the real–world CDC recommendations with 17.5 million possible strategies that also staggered the rollout in up to four phases. To calculate how well a vaccine allocation strategy performed, our model measured total deaths, cases, infections and years of life lost.
We found that the CDC allocation strategy performed exceptionally well – within 4% of perfect – in all four measures.
According to our model, the CDC’s decisions to not vaccinate children initially and prioritize health care and other essential workers over nonessential workers were both correct. But our model also showed that giving individuals with known risk factors earlier access to vaccines would have led to slightly better outcomes.
No single rollout was able to simultaneously minimize deaths, cases, infections and years of life lost. For example, the strategy that minimized deaths led to a higher number of cases. Given these limitations, the CDC plan did a good job of balancing the four goals of vaccination and was particularly good at reducing deaths.