Assessing the Uncertainty of Epidemiological Forecasts with Normalised Estimation Error Squared

11/08/2021

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by   R. E. Moore, et al.

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Estimates from infectious disease models have constituted a significant part of the scientific evidence used to inform the response to the COVID-19 pandemic in the UK. These estimates can vary strikingly in their precision, with some being over-confident and others over-cautious. The uncertainty in epidemiological forecasts should be commensurate with the errors in their predictions. We propose Normalised Estimation Error Squared (NEES) as a metric for assessing the consistency between forecasts and future observations. We introduce a novel infectious disease model for COVID-19 and use it to demonstrate the usefulness of NEES for diagnosing over-confident and over-cautious predictions resulting from different values of a regularization parameter.