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QCOVID in Scotland: time to recalibrate our expectations?
  1. Rishi K Gupta1,
  2. Maarten van Smeden2
  1. 1 Institute for Global Health, University College London, London, Greater London, UK
  2. 2 Julius Center for Health Sciences and Primary Care, Utrecht University, Utrecht, The Netherlands
  1. Correspondence to Dr Rishi K Gupta, University College London Institute for Global Health, London, WC1E 6BT, UK; r.gupta{at}

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The field of prediction modelling has exploded during the COVID-19 pandemic, with countless studies seeking to derive and/or validate models to assist diagnosis of SARS-CoV-2 or to predict clinical outcomes of infection. While the vast majority of reports have been of a poor standard,1 there have been some examples of higher quality work, including the QCOVID prognostic model.2 Whereas most prediction models are intended to predict a single outcome, QCOVID seeks to predict the risk of acquiring and then either dying from or being hospitalised with SARS-CoV-2 in the general population. The model was originally derived using data from 6.1 million people in England during the first pandemic wave (January–April 2020) and showed promising performance in a recent analysis by the Office for National Statistics over a similar time period.3 QCOVID is implemented online,4 although stipulated for research rather than clinical use, and provides distinct probabilities of COVID-19 hospitalisation and COVID-19 mortality.

Simpson et al report an external validation of QCOVID using data from Scotland over two periods (1 March 2020 to 30 April 2020 and 1 May 2020 to 30 June 2020), designed to test the performance of the algorithms during time intervals that aligned with the original QCOVID derivation and temporal validation cohorts.5 The authors use a population-based approach, including data from primary care facilities covering an impressive 99% of the Scottish population. The data provided …

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  • Twitter @Rishi_K_Gupta, @MaartenvSmeden

  • Contributors RKG wrote the first draft following discussion with MvS. Both authors approved the final submitted version.

  • Funding RKG is supported by the National Institute for Health Research, grant number DRF-2018-11-ST2-004.

  • Competing interests None declared.

  • Provenance and peer review Commissioned; externally peer reviewed.

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