All Times ET
Keywords: COVID-19, forecasting, time-series, hospital census, infection incidence
COVID-19 has been one of the most serious global health crises in world history. During the pandemic, healthcare systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. The goal of this study is to explore the potential utility of local COVID-19 infection incidence in developing a forecasting model for the COVID-19 hospital census. A multivariate time-series framework, called Vector Error Correction model, is used to simultaneously incorporate the COVID-19 hospital census and the local COVID-19 infection incidence and account for their possible long-run, i.e., cointegration, relationship. Hypothesis tests and model diagnostics are performed to examine model goodness-of-fit. 7-day-ahead forecast performance is measured by Mean Absolute Percentage Error (MAPE), with time-series cross-validation. Leading up to the peak of the pandemic, different scenarios of future infection incidence were also constructed and provided input for the fitted model to create scenario-based 60-day-ahead forecasts. From the results, the two time-series have a stable long-run relationship. The model has very good fit to the data. The typical (median) out-of-sample MAPE is 5.9%, which is lower than 6.6% from a univariate Autoregressive Integrated Moving Average model. Scenario-based 60-day-ahead forecasts exhibit concave trajectories with peaks lagging 2-3 weeks later than the peak infection incidence. Our findings show that the local COVID-19 infection incidence can be successfully incorporated into a multivariate time-series framework with the COVID-19 hospital census to improve upon existing forecast models, and to deliver accurate short-term forecasts and realistic scenario-based long-term trajectories to help healthcare systems leaders in their decision making.