 The study explored the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census using a vector error correction model, VECM framework, which simultaneously incorporates both time series and accounts for their possible long-run relationship. The results showed that the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census with very good seven days ahead forecast performance and outperformed the traditional autoregressive integrated moving average, ARIMA, model. Leveraging the relationship between the two time series, the VECM model produced realistic 60 days ahead scenario-based projections that can inform healthcare systems about the peak timing and volume of the hospital census for long-term planning purposes. This article was authored by Hugh M. Gwynne, Philip J. Turk, and Andrew D. McWilliams.