An NJIT computer scientist studied COVID vaccine data from Minnesota to design equitable methods of distributing vital resources during any widespread emergency. The resulting algorithm showed that giving everyone equal access to vital resources isn’t necessarily the best approach, depending on the methods and desires of emergency authorities, explained Pan Xu, assistant professor in Ying Wu College of Computing.

For example, if an emergency disproportionately impacted people of certain ages, ethnicities, incomes, locations or races, then those communities should get greater percentages of whichever resources were needed to help them, Xu noted. That could mean withholding the resources from other groups who are better empowered to withstand delays. To read the full story.