CSL professors Venugopal Veeravalli and Lav Varshney are seeking to improve the efficiency of these tests and determine how to quickly detect changes in the distribution of disease prevalence data through their research project, Efficient Strategies for Pandemic Monitoring and Recovery.
Most of the United States has become fairly intimately acquainted with COVID-19 testing – be it by spit, nasal swab, or blood. While this type of individual work has been effective in identifying cases, CSL professors Venugopal Veeravalli and Lav Varshney are seeking to improve the efficiency of these tests and determine how to quickly detect changes in the distribution of disease prevalence data through their research project, "Efficient Strategies for Pandemic Monitoring and Recovery."
“The goal of the project is to develop mathematical techniques that use virus testing resources more efficiently and to efficiently detect changes in the prevalence of disease in communities so as to inform control strategies like lockdowns,” says Veeravalli, Henry Magnuski Professor of Electrical and Computer Engineering.
This research includes attempting to test more efficiently by combining test samples from several people and using it for a single test. If the test is negative, then everyone is negative. If the test is positive, further testing can then be performed to determine individual infection statuses.
“The idea is to make this process even more efficient by using extra information available from symptom reporting, from alternative data sources
like wastewater monitoring, and from the social structure of people that live together, for example,” says Lav Varshney, an associate professor of electrical and computer engineering. “By taking the statistical properties of the testing procedures into account, new hotspots of disease can be identified more quickly than under traditional approaches, especially since COVID-19 cases seem to happen in clusters.”
With the actionable data taken from these tests, an outbreak detected in a certain location (such as an office) can be quashed by acting quickly. If there are repeated clusters at the same types of locations, one can design policies to stop them from happening in the first place. Group testing is already being used to monitor COVID-19 cases in other countries such as India, Germany, and Israel. This type of testing has also already been approved by the FDA for widescale use in the United States.
“If it can be done better, those efficiency gains would enable more prevalent testing -perhaps at the levels being done at the University of Illinois Urbana-Champaign, but throughout the country,” says Veeravalli. “If ‘quickest change detection’ is deployed widely to detect the spread of the pandemic, perhaps lockdown policies can be more refined and therefore the economic impacts of social distancing can be reduced.”
While this work is obviously timely during the COVID-19 pandemic, this research could be applied to future epidemics/pandemics as well. This research builds on previous work by Varshney and Veeravalli including research in quickest change detection, wastewater studies, and pathogen diagnostics. This research is supported by the NSF.