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The coronavirus disease 2019 (COVID-19) global pandemic poses the threat ofoverwhelming healthcare systems with unprecedented demands for intensive careresources. Managing these demands cannot be effectively conducted without anationwide collective effort that relies on data to forecast hospital demandson the national, regional, hospital and individual levels. To this end, wedeveloped the COVID-19 Capacity Planning and Analysis System (CPAS) - a machinelearning-based system for hospital resource planning that we have successfullydeployed at individual hospitals and across regions in the UK in coordinationwith NHS Digital. In this paper, we discuss the main challenges of deploying amachine learning-based decision support system at national scale, and explainhow CPAS addresses these challenges by (1) defining the appropriate learningproblem, (2) combining bottom-up and top-down analytical approaches, (3) usingstate-of-the-art machine learning algorithms, (4) integrating heterogeneousdata sources, and (5) presenting the result with an interactive and transparentinterface. CPAS is one of the first machine learning-based systems to bedeployed in hospitals on a national scale to address the COVID-19 pandemic - weconclude the paper with a summary of the lessons learned from this experience.

CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19