Modeling of COVID-19 total hospitalizations in the United States

Abstract

The SARS-CoV-2 (COVID-19) virus continues to increase across the globe affecting all aspects of modern life. It remains unknown whether COVID-19 hospitalizations can be effectively modeled using regression analysis. Specifically, it is unknown which regression model may accurately reflect past or future trends in COVID-19 hospitalizations. We wanted to see whether we could develop a simple model to describe both previous and future COVID-19 hospitalizations. The graph for total hospital admissions for COVID-19 shows a curve similar to a sine wave with peaks in total hospitalizations occurring in April, July, and December. We used regression analysis for total COVID-19 hospitalizations to provide insight into potential factors influencing COVID-19 hospitalizations and predict future hospitalizations. We found that the total hospitalizations in the United States followed a sine-wave distribution with peaks in hospitalizations every 3.5 months between April and November 2020. However, the sine-wave distribution for COVID-19 disappeared when the model was extended to December 2020. In general, mathematical modeling of hospitalizations works best when there is an established pattern of disease transmission from multiple years of data collection; COVID-19 is a novel virus for which we have less than a year's worth of data from which to draw conclusions. Furthermore, there remains uncertainty about the trajectory of COVID-19 cases and hospitalizations in the future, particularly with the recent emergency use authorization of the Pfizer and Moderna COVID-19 vaccines.