Analyzing factors such as population density, demographics, climate and transportation statistics could help policy-makers find strategies that could control the virus without taking a huge toll on the economy, one researcher says.
While it’s clear there’s no one-size-fits-all solution to protecting Americans and opening the economy, one researcher thinks software can help local officials devise more-granular solutions.
Sai Dinakarrao, an assistant professor in George Mason University’s Department of Electrical and Computer Engineering, believes analyzing factors such as population density, demographics, climate and transportation statistics could help policy-makers find strategies that would prevent surges in the virus without taking a huge toll on the economy, GMU officials said.
Dinakarrao, along with colleagues from University of California at Davis and Morgan State University in Baltimore, was recently awarded funding from the National Science Foundation to develop a model for pandemic, focusing on community spread, mitigation measures and the optimal distribution of health care resources in that context.
The researchers plan to develop a tool that is “generic and demography-agnostic that determines the best solution for a given topology, such as a state, county, or city,” Dinakarrao said.
Drawing from current COVID-19 data, the solution will incorporate “machine learning and stochastic optimization techniques to determine the best epidemic confinement strategy, depending on demographic information as well as the epidemic spread,” according to the award announcement.
“Given the uncertainty in the available data regarding COVID-19 due to varied testing strategies and false positives, our methodologies consider these variations to determine the optimal confinement strategy under the constraints of economic impact,” Dinakarrao said.
Because the researchers just started the year-long project, the tool will used for later waves of COVID-19, future pandemics or to mitigate bioterrorism threats. The solution is expected to have applicability beyond the current pandemic.
“We want our tool to be as scalable and futuristic as possible,” Dinakarrao said. “We want it to be able to function for any kind of pandemic.”
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