Modeling COVID-19 for Minnesota
CSE contributes to a School of Public Health study modeling the spread and impact of the novel coronavirus
Editor's note: The University of Minnesota was contracted by the Minnesota Department of Health (MDH) to help build the COVID-19 Minnesota mathematical model. The model development was led by U of M School of Public Health Associate Professors Dr. Eva Enns and Dr. Shalini Kulasingam. Enns and Kulasingam are nationally recognized modeling experts and have previously partnered on several modeling projects including studies on pertussis (in collaboration with MDH) and chlamydia in Minnesota.
The initial Minnesota COVID-19 models were created by Enns and Kulasingam. The project team includes faculty colleagues at the School of Public Health who are experts in mathematical modeling, infectious disease epidemiology, and systematic literature reviews. As part of the project team, students and research assistants support literature reviews, and help to check, confirm and expand the R programming code, which is available publicly for researchers and others to review. All work performed by students and research assistants is done under the direction and thorough review of Enns and Kulasingam. (Visit the School of Public Health website to access the R code.)
Before Friday, March 20, Marina Kirkeide was a part-time research assistant at the University of Minnesota School of Public Health (SPH), working on human papillomavirus transmission for associate professor Shalini Kulasingam. On a gap year before starting medical school at the University in fall 2020, the College of Science and Engineering alumna also had a second job as a lab tech at St. Paul’s Regions Hospital.
That Friday, Kulasingam called her and two other research assistants to ask if anyone could “work through the day and night” to get a COVID-19 model to Minnesota Governor Tim Walz the following Monday. They all jumped at the chance.
“I don’t think a lot of researchers get to work on something over the weekend and have public figures talk about it and make decisions based on it three days later,” said Kirkeide, a four-year recipient of the Patrick F. Flynn Scholarship.
Kirkeide graduated from CSE in 2019 with a bachelor’s degree in mathematics. Now, she’s part of an interdisciplinary research team—co-led by Kulasingam and SPH associate professor Eva Enns in partnership with the Minnesota Department of Health (MDH)—modeling the spread and impact of COVID-19 in Minnesota.
CSE graduate student Abhinav Mehta, who is pursuing a master’s degree in computer science, is also involved in the project.
Kirkeide and Mehta helped build custom-created mathematical models that resulted in two scenarios describing Minnesota’s future outlook related to the COVID-19 outbreak. One projection showed that cases would peak around April 26 in the state if there were no mitigating steps to slow the virus. The death toll in this scenario could reach 74,000. The other scenario showed a time frame with significant and staged mitigations in place that pushed the peak to about June 29 and projected deaths in the 50,000–55,000 range.
Governor Walz relied heavily on these projections when he made his decision to issue a stay-at-home order on March 27. Technical information and source material related to the models are available on the MDH website.
How to build a model for a brand new disease
Models carry tremendous weight during any global crisis and, depending on how they influence policymakers or even individuals, can make the difference between life and death. Used for COVID-19, they’re meant to show how things can change with varying levels of mitigation in place or ICU beds available, which can move peaks in infections and deaths closer to or further out from the present.
“People throw around the word ‘model’ a lot and there are a lot of different approaches,” said Enns, who has shifted her research toward helping Minnesota prepare for when COVID-19 cases surge drastically upward. “We use a mathematical model with equations to represent the mechanism of how infection spreads."
The classic equation for modeling infectious diseases such as COVID-19 is called an SEIR model (Susceptible, Exposed, Infectious, Recovered). The Minnesota model adds three more stages representing people who are: hospitalized, in ICU, or dead. The model uses mathematical equations to describe how people move in and out of each stage over time.
Into the model also go various other elements to more accurately predict progression and spread, such as age, demographics, and underlying conditions. Then researchers run the model to see how the disease mechanism might change with different policies in place, for example, two-, four- or six-week shutdowns or cycling stay-at-home policies on and off. The team uses estimates from China and Europe to craft some parameters for the Minnesota model because U.S. data is still limited. But the model will become ever more state specific as U.S. data grows and Minnesota-centric information becomes more robust.
The team has nearly completed building a dynamic interface for the model, and when it’s sufficiently robust to quickly produce results and generate validated output, they will make it available to the public along with the underlying code. The goal for that release is later in April.
Kirkeide, who left her hospital job to focus solely on modeling, feels the responsibility and pressure of such a big project.
“[In this situation] you don’t have the time to validate as much as you normally would,” she said.
“You want to get it right the first time," she said. "And your work has to be really, really quick.”
Although coronaviruses themselves are not new, each variation of the virus, such as SARS, MERS, and now COVID-19, have no historical precedent, and that makes modeling COVID-19 extra difficult. Kirkeide offers an example of how hard it is to work with unknowns.
“We have a hospitalization count for people with COVID-19 based on age, and we want to put this in the model in a way that makes sense,” she said. “But we can’t use those numbers to figure out a percentage of those hospitalized with COVID-19 at that age, because we don’t know how many people have undetected illness. So we can’t modify a percentage over time in our model if we don’t have a known total.”
Social distancing and stay-at-home orders are the critical strategies to keep people safe across all COVID-19 models, but we know very little about them in practice.
Enns said that to refine COVID-19 model predictions for Minnesota, it will be good to know how well we can do social distancing and how long we can do it well. Can we last a month or six weeks? Do we get better at it as time goes on or does compliance wane? A survey the team plans to conduct with MDH may get at that information as it asks people how their contact patterns have changed since Minnesota’s Stay Home Order.
“We’re figuring out ways to compare what we’re actually seeing with what our model is predicting, then adjusting assumptions about [the course of the virus],” Enns said.
Models offer guidance and show possibilities, and all model makers are quick to add that no numbers are certain. “Models are not crystal balls,” said Stefan Gildemeister, Minnesota’s State Health Economist, who has been the team’s MDH partner in the modeling project.
“We really want to report things as a range,” Enns said. “We need to build in uncertainty as a clear message."
"The trends and impacts of the disease are fairly stable," she said. "The exact timing and number of cases are very uncertain.”
Modeling is a dynamic exercise, and the Minnesota team is continuing to refine the model day by day as new information becomes available so they can give Walz a more accurate appraisal of the state’s current situation and possible progress. What is certain is that the team will do its best job.
As a modeler, Kirkeide said, you have complete control over what your results look like. The most important thing is to have absolute integrity.
“Yes, numbers may look grim, but they are what we’re getting,” she said. “You can’t argue with what you see.”
Reprinted with permission from the U of M School of Public Health. View the original story.