Forecasting Ultra-early Intensive Care Strain from COVID-19 in England, v1.1.4

  1. Jacob Deasy
  2. Emma Rocheteau
  3. Katharina Kohler
  4. Daniel J. Stubbs
  5. Pietro Barbiero
  6. Pietro Liò
  7. Ari Ercole

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Abstract

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 require complex local and national service reconfiguration and reduction or cancellation of elective activity. These measures have an inevitable lag-time 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. If this represents a true spread in disease then ICU demand could increase rapidly. Here we present a short-range forecast based on published real-time COVID-19 case data from the seven National Health Service (NHS) 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 under the assumption that the increase in cases represents the start of an exponential growth in infections. Our model is designed to be parsimonious and based on available epidemiological data from the literature at the moment.

On the basis of the modelling assumptions made, ICU occupancy is likely to increase dramatically in the days following the time of modelling. If the current exponential growth continues, case numbers will be comparable to current ICU bed numbers within weeks. 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. Our findings should be interpreted with caution, but 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.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.19.20039057: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe higher male:female ratio in China, according to data from the 2017 Chinese census [11], required us to adjust our calculations under the assumption that the added risk of being male holds on a univariate basis.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, we do recognise that as with all models our is subject to significant assumptions and limitations. Both model and parameter uncertainties are inevitable, particularly when predicting the behaviour of a novel virus in a new population, and this may radically affect our forecasts. Nevertheless, we set out to provide the earliest possible data-driven forecast and therefore explicitly accept the limitations of the data that were available to us at the time. Our approach has been to keep the model as parsimonious as possible, with parameter estimates from the existing literature, to give a rough guide to early surge needs. With the small amount of available data, there are some specific limitations which need to be discussed. Our use of an updated, online resource we hope is a novel way of allowing interested parties to interact with the mathematical assumptions and formulate their own opinions. We have used PHE published data for case ascertainment. We recognise that this data is potentially flawed and does not recognise all cases within the wider population. However, being as our explicit aim is to model the most severe cases, the effect of any ascertainment bias on our findings should be minimised due to the rollout of routine testing for all critical care patients as part of the “ COVID Hospitalisation in England Surveillance System—CHESS” [18]. Despite this, case ascertainment and delay between symptom onset and ICU admission is unlikely to be uniform between populat...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.03.19.20039057v3 on medRxiv
  3. Version published to 10.1101/2020.03.19.20039057v2 on medRxiv
  4. Version published to 10.1101/2020.03.19.20039057v1 on medRxiv