A pair of University of Louisiana at Lafayette mathematicians has received nearly $200,000 from the National Science Foundation to examine the effectiveness of social distancing and other measures that aim to prevent the spread of COVID-19.
Dr. Cameron Browne and Dr. Hayriye Gulbudak are assistant professors in the at UL Lafayette. Both use mathematical modeling to predict the spread of infectious diseases.
Mathematical models replicate real-life situations, and use equations and data to predict future behavior. Models also enable researchers to fill in gaps when contemporary data is incomplete or unavailable.
In their proposal to NSF, Browne and Gulbudak wrote that modeling and analysis “can provide important insights into the efficacy of contact-based, non-pharmaceutical interventions.”
These include quarantining, social distancing and contact tracing, which lessens the spread of COVID-19 by proactively identifying people who are at a higher risk than others because of potential exposure to the virus.
However, modeling “requires detailed case data, which is often challenged by inconsistent, unreported and asymptomatic cases,” the researchers continued.
To counter this, Browne and Gulbudak will develop models that incorporate numbers of reported cases, as well as data about how the virus has mutated and migrated. Studying both could provide a more-accurate picture of COVID-19’s effect, they said.
Their work also may help public health officials and policymakers determine “how to best implement contact-based measures for effectively containing” COVID-19 and mitigating any future outbreaks.
The $199,000 NSF grant Browne and Gulbudak received is funded by the Coronavirus Aid, Relief and Economic Security, or CARES, Act.
U.S. Sen. John Kennedy of Louisiana announced the grant Monday.
“With accurate data, we can understand what works best to combat the spread of the coronavirus. I’m glad to see this funding support Louisiana research to flatten the curve,” Kennedy said in a press release.