Forecasting ultra-early intensive care strain from COVID-19 in England

Deasy, Jacob; Rocheteau, Emma; Kohler, Katharina; Stubbs, Daniel J.; Barbiero, Pietro; Liò, Pietro; Ercole, Ari

The COVID-19 pandemic has led to unprecedented strain on intensive care unit (ICU) admission in parts of the world. Strategies to create surge ICU capacity requires complex local and national service reconfiguration and reduction or cancellation of elective activity. Theses measures require time to implement and have an inevitable lag before additional capacity comes on-line. An accurate short-range forecast would be helpful in guiding such difficult, costly and ethically challenging decisions. At the time this work began, cases in England were starting to increase. Here we present an attempt at an agile short-range forecast based on published real-time COVID-19 case data from the seven National Health Service commissioning regions in England (East of England, London, Midlands, North East and Yorkshire, North West, South East and South West). We use a Monte Carlo approach to model the likely impact of current diagnoses on regional ICU capacity over a 14 day horizon. Our model is designed to be parsimonious and based on plausible epidemiological data from the literature available. On the basis of the modelling assumptions made, ICU occupancy is likely to increase dramatically in the the days following the time of modelling. If the current exponential growth continues, 5 out of 7 commissioning regions will have more critically ill COVID-19 patients than there are ICU beds within two weeks\todo{last thing to do}. Despite variable growth in absolute patients, all commissioning regions are forecast to be heavily burdened under the assumptions used. Whilst, like any forecast model, there remain uncertainties both in terms of model specification and robust epidemiological data in this early prospective phase, it would seem that surge capacity will be required in the very near future. We hope that our model will help policy decision makers with their preparations. The uncertainties in the data highlight the urgent need for ongoing real-time surveillance to allow forecasts to be constantly updated using high quality local patient-facing data as it emerges.