Computer modeling quantifies the importance of COVID-19 vaccination to control the pandemic

New research from Mayo Clinic data scientists shows how critical a high vaccination rate is for reducing case numbers and controlling the pandemic. They developed highly accurate computer models to forecast patterns for COVID-19 cases nationally.

According to a report published in Mayo Clinic Proceedings, vaccination is making a significant difference in Minnesota, preventing the current number of positive cases from being an epidemic that overwhelms ICUs and leads to more illness and death. The report, titled “Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook,” explains how Mayo Clinic’s COVID-19 predictive modelling can forecast future patterns based on vaccination rates, and how vaccination rates are critical to the pandemic’s future path.

If no vaccines had been produced, the Mayo researchers estimate that over 800 patients will be in Minnesota hospital ICUs this spring. New SARS-CoV-2 virus strains, as well as existing public health initiatives and masking criteria, are factored into the predictions.

The expected ICU census levels would be more than double the number of Minnesota COVID-19 patients admitted to ICUs on Dec. 1, when the most recent surge occurred last year.

“It’s hard to tell how much of this increased rate of spread is due to new variants versus improvements in social behaviour right now,” the authors write, but “regardless of the cause, the lack of vaccines in the current setting would have certainly resulted in by far the largest surge to date.”

The study predicts that if 75 percent of Minnesota’s population had been vaccinated by early April, the 7-day average of cases per 100,000 people, the number of COVID-19 patients hospitalised, and the number in ICUs will all drop by early July. “This level of vaccination, according to the model, will fully eradicate the growth (even in the face of the recent elevated spread rate) and instantly drive cases and hospitalizations to very low levels,” the authors write.

Curtis Storlie, Ph.D., and Sean Dowdy, M.D., of Mayo Clinic, led the research, and their team developed the computer model for forecasting COVID-19’s effect on hospital use, which has aided Mayo’s response to the pandemic. Mayo Clinic’s predictive modelling was also shared with Minnesota public health officials last year to help them make important decisions.

COVID-19 patterns forecasted by Mayo Clinic are available online at the Mayo Clinic COVID-19 Resource Center. The Coronavirus Map monitoring tool provides county-by-county data on COVID-19 cases and patterns throughout the United States.

Mayo Clinic data scientists developed predictive modelling to predict when and where COVID-19 hot spots would occur when the pandemic broke out last year. Mayo Clinic was able to plan to ensure that it could deliver the best treatment while keeping patients and staff healthy because the model correctly forecast the timing and severity of COVID-19 case and hospitalisation spikes.