Published May 11, 2020
While most people across the nation rely on government officials for critical information regarding COVID-19, officials in one city are turning to a UB student for guidance.
Monica Rogers, a doctoral candidate in information science in the Graduate School of Education, is the information systems manager for the Tulsa Health Department.
Rogers monitors infections, hospitalizations and deaths related to COVID-19 to craft data models that provide crucial information on the peak spread of the virus and to project future rates of illness. These models assist key decision-makers in the city of Tulsa and the state of Oklahoma.
“How many people are sick? How many will die? When will those things happen? These are the questions I try to answer in my models,” says Rogers, who enrolled in the UB graduate program to expand her skillset in data science. “We work with decision-makers to show different scenarios and their outcomes, such as what would happen if we allowed limited social gatherings or lifted restrictions.”
The pandemic is the first time she has been tasked with modeling infectious disease; prior to the outbreak, she mostly forecasted community health needs and the prevalence of chronic conditions.
A challenge of creating models around COVID-19 is that essential information is constantly changing or doesn’t exist, says Rogers, who has worked in information science for more than a decade.
“People should understand that we’re dealing with a new disease, so there are a lot of assumptions and a severe lack of good data. We’re assuming people are social distancing; we’re assuming people can’t get re-infected,” she explains. “The earliest models have the widest ranges of potentials. It’s an iterative process. As we gain better data, we can tighten up projections so that they’re closer to reality.”
Rogers reviews and adjusts her models each week. While new information, particularly from mass testing and the ability to better track the spread of infection, would improve her estimates, human behavior is the greatest influence on her projections, she says.
“Models are not written in stone. They assume how people will behave. When their behavior changes, the model is no longer accurate,” says Rogers. “Even if the model projected that the peak has already passed, that assessment was made with the assumption that people will continue to follow government guidelines.”