Nearly two years ago, the U.S. Centers for Disease Control and Prevention released its recommendations for a phased COVID-19 vaccine rollout. The agency prioritized groups based on occupation, age, living conditions, and high-risk medical conditions in an effort to protect the vulnerable and reduce deaths.
But Claus Kadelka, an assistant professor of mathematics at Iowa State University and lead author of a new paper in the Journal of Theoretical Biology, says future pandemic responses should also consider ethnicity and social contact patterns that affect disease dynamics.
The researchers point to multiple studies showing that people of color have been disproportionately affected by COVID-19. The infection rate in predominantly Black counties in the U.S. was three times higher than in predominantly White counties in 2020. One reason for the disparity, they explain, is that people of color are more likely to work in public-facing and high-contact jobs (e.g., transportation services, grocery stores, meat packing facilities), which do not easily allow for physical distancing and remote work. People of color are also more likely to live in higher-density or multi-generational housing where it’s harder to quarantine and prevent the spread of the virus.
Using data from the Centers for Disease Control and Prevention, U.S. Census Bureau, and U.S. Bureau of Labor Statistics, the researchers applied a model they developed last year and incorporated different contact rates and occupational hazards by age and ethnicity. They then utilized the Iowa State supercomputer to analyze 2.9 million different vaccination strategies to identify those that achieved specific goals such as lower rates of infection and a lower number of deaths.
Dr. Kadelka said that “the best strategy that included ethnicity prevented more deaths than the best strategy without ethnicity. When trying to minimize the number of deaths, it is best to get the vaccine to the oldest people of color first.”
As to whether the optimal vaccine strategy in the model would have had the same effect in the real world, Dr. Kadelka said it’s hard to know because many social and disease characteristics, like detailed contact patterns and factors affecting susceptibility to infection, are still poorly understood.