Engineers, doctors team up to study pooled COVID-19 testing


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Hadi Meidani
Hadi Meidani
A new method of COVID-19 testing that pools samples from multiple people to speed up population screening when test kits are scarce is being investigated by a team led by CEE assistant professor Hadi Meidani. Using computer models and lab experiments, the team hopes to develop the most effective and inexpensive way to employ pooled testing to help decision-makers understand the prevalence of COVID-19 in their communities or workforce and make informed responses for public safety. 

“The lack of available testing capacity, and the resulting inability to broadly test the community at large scale leaves decision makers with essentially no information about the overall prevalence of the virus in the general community,” Meidani said. “Confirmed case numbers, reported daily in the news, vastly understate the actual extent of the spread of the virus.”

Meidani’s collaborators are John Farrell, a physician and infectious disease specialist who is directing the COVID-19 response team at OSF Healthcare in Peoria, Ill., and Daniel Lakeland, Founder and Research Analyst at Lakeland Applied Sciences LLC, who will collaborate with Meidani on creating decision support systems for public health administrators.


John Farrell (left) and Daniel Lakeland

COVID-19 is typically diagnosed using Polymerase Chain Reaction (PCR) which involves creating billions of DNA copies to detect the presence of a unique genetic sequence from viruses in a sample taken from a patient. The test takes several hours. The current lack of availability of test kits has caused critical delays in identifying infected cases and has hindered time-sensitive community testing and mitigation strategies due to lack of information about how widespread the disease actually is in the U.S. as a whole, in local regions, or in smaller populations such as a food processing workforce.

A pooled testing approach combines samples from a large group of patients, and for low infection rates can very accurately estimate the prevalence with a fraction of the testing capacity. Furthermore, because the method tracks which samples are included in the batches, individual patients who are at high risk of being infected can be identified for follow-up testing and treatment plans if a positive result is found in a batch.

The team’s goal is to investigate a pooled community testing approach which optimally estimates the prevalence of COVID-19 in a community while running only a limited number of PCR-based tests. This would provide local decision-makers with the information they need to manage interventions like school closures and shelter-in-place orders.

The study consists of two phases. In the current phase, researchers are performing pooled PCR tests based on actual samples obtained from patients at the OSF System Microbiology lab. They are also running computer simulations to assess the effectiveness of pooling-based community testing under different scenarios.  In Phase 2, expected to begin in 6 months, the team hopes to collaborate with local public health departments to develop a comprehensive optimization and decision support tool that enables public health officials to administer pooled community screening with maximum information gain and minimal cost. That phase will also involve computer models for mobility and social interactions of a community that will predict hotspots of virus spread and accordingly guide future pooled testing.

This work is supported by a research grant from the program, Jump Applied Research for Community Health through Engineering and Simulation (Jump ARCHES), a strategic partnership of University of Illinois’s The Grainger College of Engineering that supports collaborative efforts between engineers and health care providers at OSF HealthCare and the University of Illinois College of Medicine at Peoria as they work to solve health care challenges through innovative solutions. 

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This story was published June 29, 2020.