Toward a Fairer Vaccine

A vaccine fairness recommendation engine will support equitable decision-making about vaccination. Bryce Richter

Mathematical models have helped the U.S. optimize COVID-19 vaccine allocation and delivery to boost vaccination rates. But these models have not overcome the existing health disparities that stem from unequal access to health care; discrimination; and gaps in education, income, and wealth attainment.

“There’s a missing step between the mathematics and the reality,” says Michael Ferris, the John P. Morgridge Professor of Computer Sciences. “You can solve problems with mathematics up to the last mile, but at that point behavior, communication, and socioeconomic issues become critical.”

Ferris and Corey Jackson, assistant professor at the UW–Madison Information School, are developing a vaccine fairness recommendation engine that will support equitable decision-making about vaccination, with the goal of increasing immunization rates.

“Access is not just being within five miles of a vaccination site,” says Jackson. “It also means, do you have the ability to take off work to go and get the vaccine? Does the location that’s closest to you actually have appointments available? If you speak Spanish at home, is the app for making appointments translatable?”

Jackson and Ferris are measuring whether geographic areas identified as socially vulnerable by the Centers for Disease Control and Prevention are receiving fair allocations of vaccines. They will then catalog interventions and measure how well these interventions are working.

“I think we’ll have a better idea about what fairness in medical or health decision-making looks like,” says Jackson. “My hope is this work will provide useful information for decision-makers moving forward.”

Published in the Winter 2021 issue