An epidemic spreads differently through the rural Midwest than through a big city. So University of Dayton researcher Subramanian Ramakrishnan will use $231,185 of a $650,000 National Science Foundation grant to develop better models to predict the spread locally and regionally rather than a one-size-fits-all national approach.
"It might be too ambitious to try one model for the entire country, any country," said Ramakrishnan, UD associate professor of mechanical and aerospace engineering. "It's not like constructing a universal model for the laws of gravity, which apply to everything in the universe. The dynamics of epidemics differ by location, so creating models specific to the dynamics in question is very important.
"The limitations of existing predictive models, as evident during the COVID-19 outbreak in the U.S., underscore the need for new knowledge in this area. As we saw, everything was marshaled on a war footing to respond early to COVID-19, and yet we were not prepared enough or, at least we could have been much better equipped with fundamental knowledge."
Ramakrishnan, project lead, will be working with University of Cincinnati mechanical engineering professor Manish Kumar, and Shelley Ehrlich, a medical doctor and epidemiologist at Cincinnati Children’s Hospital Medical Center. They will focus on how uncertainties in human behavior and disease transmission drive an epidemic and the challenges of prediction, given the limitations of procuring reliable early data. Even within local areas, they want to produce a suite of models that range from best-case to worst-case scenarios in the short-term and the medium-term.
"You're not entirely sure how much to trust early data. There's always that inevitable time lag between an infection spread happening on the ground and the public health system collecting that data," Ramakrishnan said. "So we also need to create models that can be flexible enough to interact with and learn from the data in as close to real time as possible."
Doing so will allow local and regional public health officials to appropriately prescribe early interventions like physical distancing and masking, according to Ramakrishnan.
To secure the grant, the group showed a proof of concept using COVID-19 transmission data from the state of Ohio and Hamilton County, Ohio, for the month of April 2020. They found their preliminary models worked well for the state data and even better for county data, which Ramakrishnan said "reinforces this notion that these models are probably more effective for smaller geographical regions, which is again a very important thing to know."
Two UD graduate students will assist in the research, running models and simulations and performing mathematical analysis under the team's guidance. They also have the opportunity to be listed as authors in articles publishing results of the research. Ramakrishnan hopes to add UD undergraduate students by applying for an NSF Research Experience for Undergraduates grant.
"Advances in research would be much slower and much less exciting without the contributions of student researchers," Ramakrishnan said. "Academic research thrives on the opportunity to inspire and train students. It's a fundamental part of what we do."
Click here to read the NSF's complete award abstract. For more information on the research, contact Ramakrishnan at sramakrishnan1@udayton.edu.
For interviews, please contact Shawn Robinson, associate director of news and communications, at srobinson1@udayton.edu.
Original source can be found here.
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