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Grant helps URMC researchers' math models predict how soon pandemic could end

Coronavirus_particles.jpg
This transmission electron micrograph shows SARS-CoV-2 virus particles isolated from a patient suffering from COVID-19. The image was captured and color-enhanced at the NIAID Integrated Research Facility in Fort Detrick, Maryland.

(WHAM) - Two researchers at the University of Rochester are using a mathematical model to help monitor how the novel coronavirus is spreading - and how quickly the pandemic could end.

A grant from the National Science Foundation awarded to Gourab Ghoshal, an associate professor of physics, mathematics, and computer science, and Andrew White, an assistant professor of chemical engineering, will allow the two to create a tool that could potentially be used by local and state governments to make decisions on reopening.

Ghosal and White specialize in epidemiology and molecular simulation.

The model focuses on the rate of infection, often represented by the term “R0”. If R0 equals two, this means for each single person infected with coronavirus, two others will be infected. The higher R0 is, the more quickly it is spreading. Eventually other factors, such as social distancing, wearing masks, and sanitizing hard surfaces, affect the infection rate.

As more factors influence the infection rate, it becomes more difficult to predict. Researchers can use some of those factors to calculate the rate of infection, including people who are susceptible or exposed to the virus, asymptomatic but infectious, and symptomatic and infectious patients, as well as recovered and deceased patients. In their research, Ghoshal and White plan to use their model to create 'R effective' - which is a constantly changing rate of the number of people who could be infected by a person at any given time.

“That is good because it means you’re in a situation where the pandemic might die down, but then the moment you start easing lockdowns, there’s a good chance that can take off again,” Ghoshal says. “Our model might be able to give you what R effective is in real time.”

Ghoshal and White plan to use a method called maximum entropy biasing - centered around the idea that the number of factors influencing the infection rate is unknown, so using a bias term helps to account for things that scientists don’t know. Combining maximum entropy biasing with mortality rates and hospitalization rates in a given area will give community leaders the 'R effective' number to help them make decisions.

The goal of Ghoshal and White’s model will be to monitor infection rates on a day-to-day basis and to use it to help determine whether opening businesses causes infection rates to spike.

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