Use of Community SARS-CoV-2 Case Counts and Instantaneous Reproductive Number to Predict Short-Term COVID-19 Hospital Admission Volumes

Abstract

The severe acute respiratory syndrome (SARS-CoV-2) pandemic and high hospitalization rates placed a tremendous strain on hospital resources, necessitating the use of models to predict hospital volumes and the associated resource requirements. Complex epidemiologic models have been developed and published, but many require continued adjustment of input parameters. We developed a simplified model for short-term bed need predictions that self-adjusts to changing patterns of disease in the community and admission rates. The model utilizes public health data on community new case counts for SARS-CoV-2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV-2 in New York, New York (October 2020-April 2021) for its accuracy in predicting numbers of coronavirus disease 2019 (COVID-19) admissions 3, 5, 7, and 10 days into the future, comparing predicted admissions with actual admissions for each day at a large integrated health-care delivery network. The mean absolute percent error of the model was found to be low when evaluated across the entire health system, for a single region of the health system or for a single large hospital (6.1%-7.6% for 3-day predictions, 9.2%-10.4% for 5-day predictions, 12.4%-13.2% for 7-day predictions, and 17.1%-17.8% for 10-day predictions).

Keywords: COVID-19; SARS-CoV-2; admission prediction; coronavirus disease 2019; hospital admission; predictive modeling; reproductive number; severe acute respiratory syndrome coronavirus 2.