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 allaspects of modern life. It remains unknown whether COVID-19 hospitalizations can be effectivelymodeled using regression analysis. Specifically, it is unknown which regression model mayaccurately reflect past or future trends in COVID-19 hospitalizations. We wanted to see whetherwe 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 withpeaks in total hospitalizations occurring in April, July, and December. We used regression analysisfor total COVID-19 hospitalizations to provide insight into potential factors influencing COVID-19hospitalizations and predict future hospitalizations. We found that the total hospitalizations in theUnited States followed a sine-wave distribution with peaks in hospitalizations every 3.5 monthsbetween April and November 2020. However, the sine-wave distribution for COVID-19 disappearedwhen the model was extended to December 2020. In general, mathematical modeling ofhospitalizations works best when there is an established pattern of disease transmission frommultiple years of data collection; COVID-19 is a novel virus for which we have less than a year’sworth of data from which to draw conclusions. Furthermore, there remains uncertainty aboutthe trajectory of COVID-19 cases and hospitalizations in the future, particularly with the recentemergency use authorization of the Pfizer and Moderna COVID-19 vaccines.Keywords: SARS-CoV-2, COVID-19, hospitalizations, ventilator, morbidity, and mortality