The COVID-19 vaccine rollout has been remarkable and unprecedented in scale and ambition, protecting lives, livelihoods, and communities across the globe. But it’s not without its challenges, especially in underserved communities.
Vaccines are one of medical science’s greatest achievements. They allow scientists to take a new pathogen, figure out how to trigger an immune response against it, then develop and test a safe vaccine for the virus within a matter of months—the fastest such process ever recorded.
But just as a good vaccine takes time to become effective, a bad vaccine can be deadly. Despite the tremendous progress made so far, we must continue to strive for vaccine safety and equity in the future. This will require the continued support of CDC’s Data Modernization Initiative (DMI), which is central to ensuring that a robust and reliable information system can be used to provide rapid insights to health departments, public health laboratories, the military, local governments, and private sector partners.
To “roll out” a vaccination program means to get it in front of as many people as possible as quickly as possible, using a variety of strategies. In the case of the current vaccination effort, it’s a combination of mass-vaccination sites and community outreach efforts.
In this article, we use a SEIRD model segmented by population age groups and prioritised by mortality to compare different behavioural strategies for vaccine allocation in terms of their ability to reduce the number of fatalities at different daily vaccination rollout rates. Compared to no group prioritisation, the strategy that vaccinates groups in decreasing order of mortality appears to be slightly worse at high vaccination rate but substantially better at low vaccination rates and with higher coverage. Prioritising the oldest groups first also yields some significant reductions in fatalities.