Continuously updated forecasting of SARS-CoV-2 in a regional health system.

OBJECTIVES: To build a model of local hospital utilization resulting from SARS-CoV-2 and to continuously update it with new data. STUDY DESIGN: Retrospective analysis of real performance resulting from a model deployed in a major regional health system. METHODS: Using hospitalization data from the Kaiser Permanente Mid-Atlantic States integrated care system during the period from March 10, 2020, through December 31, 2020, and a custom-developed genetic particle filtering algorithm, we modeled the SARS-CoV-2 outbreak in the mid-Atlantic region. This model produced weekly forecasts of COVID-19-related hospital admissions, which we then compared with actual hospital admissions over the same period. RESULTS: We found that the model was able to accurately capture the data-generating process (weekly mean absolute percentage error, 10.0%-48.8%; Anderson-Darling P value of .97 when comparing percentiles of observed admissions with the uniform distribution) once the effects of social distancing could be accurately measured in mid-April. We also found that our estimates of key parameters, including the reproductive rate, were consistent with consensus literature estimates. CONCLUSIONS: The genetic particle filtering algorithm that we have proposed is effective at modeling hospitalizations due to SARS-CoV-2. The methods used by our model can be reproduced by any major health care system for the purposes of resource planning, staffing, and population care management to create an effective forecasting regimen at scale.