Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence

• Published in: JMIR public health and surveillance Vol. 7; no. 8; p. e28195
• Main Authors: Nguyen, Hieu M, Turk, Philip J, McWilliams, Andrew D
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 04-08-2021
• Subjects: Census; Censuses; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - therapy; COVID-19 diagnostic tests; Disease transmission; Forecasting; Forecasting - methods; Global health; Health care; Health surveillance; Hospitals; Humans; Incidence; Infections; Intensive care; Long-term planning; Medical screening; Metropolitan areas; Models, Theoretical; Multivariate Analysis; North Carolina - epidemiology; Original Paper; Pandemics; Patient admissions; Public health; Regression analysis; Severe acute respiratory syndrome coronavirus 2; Time series; North Carolina; United States > US; COVID-19; infection incidence; time-series model; forecasting; hospital census; vector error correction model; hospital resource utilization
• ISSN: 2369-2960; 2369-2960
• DOI: 10.2196/28195

COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care 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 was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census.
The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts.
The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave.
When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.