By David Staudacher

When a disaster strikes, emergency resources are often distributed proportionately based on the population size of the areas impacted.

But what if resources are limited, and all subgroups’ needs are not equal? That is what Hadis Anahideh, a research assistant professor in mechanical and industrial engineering at UIC, and her team considered when they began investigating methods used to distribute COVID-19 vaccines.

Focusing on equity of distribution instead of only equality, the researchers evaluated COVID-19 datasets from three major segregated cities in the United States — Chicago, New York and Baltimore. All three cities have relatively high Hispanic and Black populations, and several studies during the pandemic have shown a higher rate of COVID-19 infection among Black and Hispanic communities, which are more vulnerable due to social, economic and other factors affecting their exposure rates.

The team designed a “fairness-aware” vaccine allocation approach to maximize equality and avoid inequity by balancing the tradeoff between geographical diversity and social group fairness. The team’s approach showed how to allocate limited resources, such as vaccinations, to different regions at the ZIP code level.

Their framework showed how incorporating fairness in vaccination allocation modeling and distribution can “change the game,” Anahideh said.

“If you consider the fairness notion in allocation modeling, some regions will be prioritized that otherwise wouldn’t be on the top of the list for allocation. That was a major contribution that we showed both theoretically and empirically using real COVID-19 datasets,” Anahideh said. “The whole idea is to distribute limited resources so that we can protect vulnerable populations. Considering fairness, our framework prioritized regions with a higher proportion of Hispanic and Black populations, which would be missed without considering fairness.”

The framework can assist policymakers in developing strategies that prioritize vulnerable populations in medical resource allocation and distribution.

“The resource allocation in the U.S., mainly the infrastructure for distribution, is insufficient to provide a universal supply of vaccinations and treatment within a short period of time,” she said. “Targeting the high-risk population and vulnerable population and designing fair principles for prioritizing these regions and communities for allocation is significantly important.”

The results of the research were published in the Socio-Economic Sciences Journal.

Learn more about Anahideh’s research at Optimal Learning and Exploration Laboratory.