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.
翻译:传染病模型的估算是用于指导联合王国应对COVID-19大流行的科学证据的重要组成部分,这些估算在精确度上可能差别很大,有些过于自信,另一些则过于谨慎。流行病学预测的不确定性应当与其预测中的错误相称。我们提议将标准化估计错误方格(NEES)作为评估预测与未来观测之间一致性的衡量标准。我们为COVID-19引入了一个新的传染病模型,并用它来证明NEES对诊断由规范参数的不同值产生的过度自信和过度谨慎预测的有用性。