High Vaccination Rate Key to Controlling the Pandemic, Modeling Shows
The model takes into account new COVID-19 variants, as well as current public health measures and masking standards
The Mayo Clinic data scientists who developed highly accurate computer modeling to predict trends for COVID-19 cases nationwide have new research that shows how important a high rate of vaccination is to reducing case numbers and controlling the pandemic.
Vaccination is making a striking difference in Minnesota and keeping the current level of positive cases from becoming an emergency that overwhelms ICUs and leads to more illness and death, according to a study published in Mayo Clinic Proceedings. The study outlines how Mayo's COVID-19 predictive modeling can assess future trends based on the pace of vaccination, and how vaccination trends are crucial to the future course of the pandemic.
The Mayo researchers estimate that a peak of more than 800 patients would be in hospital ICUs in Minnesota this spring if no vaccines had been developed. The projections take into account new variants of the SARS-CoV-2 virus as well as current public health measures and masking standards.
The predicted ICU census levels would be more than double the number of Minnesota COVID-19 patients who were hospitalized in ICUs on December 1, 2020, at the height of the most recent surge last year.
"It is difficult to untangle how much of this elevated rate of spread right now is due to new variants as opposed to changes in social behavior," the authors say, but "regardless of the reason, the absence of vaccinations in the current environment would have been likely to result in by far the largest surge to date."
If Minnesota had achieved vaccination of 75 percent of the population by early April, the study estimates that the 7-day average of cases per 100,000 residents, the number of COVID-19 patients hospitalized, and the number in ICUs would plummet by early July. "According to the model, this level of vaccination would completely suppress the growth (even in the face of the recent elevated spread rate) and immediately drive cases and hospitalizations down to very low levels," the authors say.
The Mayo Clinic study was led by Curtis Storlie, PhD, and Sean Dowdy, MD, whose team developed the computer model for forecasting COVID-19's impact on hospital usage that has helped guide Mayo's response to the pandemic. Mayo Clinic's predictive modeling also has been shared with Minnesota public health leadership to help inform critical decisions over the past year.
Mayo Clinic's forecasting of COVID-19 trends nationally is available online at the Mayo Clinic COVID-19 Resource Center. The Coronavirus Map tracking tool has county-by-county information on COVID-19 cases and trends nationwide.
When the pandemic emerged last year, Mayo Clinic data scientists developed predictive modeling to assess when and where COVID-19 hot spots would occur. The model accurately predicted the timing and magnitude of COVID-19 case and hospitalization surges, which enabled Mayo Clinic to prepare and assure it could provide the best care while keeping patients and staff safe.
- This press release was originally published on the Mayo Clinic website