Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Feb 24, 2021
Date Accepted: Jun 29, 2021
Forecasting COVID-19 Hospital Census: A Multivariate Time-series Model Based on Local Infection Incidence
ABSTRACT
Background:
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.
Objective:
The goal of this study is to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census.
Methods:
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 relationship. Hypothesis tests and model diagnostics are performed to test for the long-run relationship and examine model goodness-of-fit. 7-day-ahead forecast performance is measured by Mean Absolute Percentage Error, with time-series cross-validation. Based on different scenarios of the pandemic, the fitted model is leveraged to produce 60-day-ahead forecasts.
Results:
There is sufficient evidence that the two time-series have a stable long-run relationship, which serves as an important predictor. The model has very good fit to the data. The typical (median) out-of-sample Mean Absolute Percentage Error 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.
Conclusions:
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.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.