LAUSR

The SARS-CoV2 pandemic and high hospitalization rates placed a tremendous strain on hospital resources necessitating 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-CoV2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV2 2 in New York (October 2020-April 2021) for its accuracy in predicting number of COVID-19 admissions at three, five, seven and 10 days into the future comparing predicted admissions with actual admissions for each day at a large integrated healthcare delivery network. 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 five-day predictions, 12.4%-13.2% for seven-day predictions, and 17.1-17.8% for 10-day predictions).