论文标题
COVID-19：使用有偏见的病例测试数据来实现复制因素
COVID-19: Nowcasting Reproduction Factors Using Biased Case Testing Data
论文作者
论文摘要
及时估算Covid-19的当前价值$ r $ $ r $已成为为管理策略提供努力的关键目的。 $ r $是政策制定者在设定缓解水平的重要指标，对于准确的流行进展建模也很重要。这篇简短的论文介绍了一种从偏见的病例测试数据中估算$ r $的方法。使用测试数据而不是住院或死亡数据，沿着症状的进展量表提供了较早的度量。在与流行病的指数性质作斗争时，这可能非常重要。我们开发了一个实用的估计器，并将其应用于苏格兰案例测试数据以推断当前（2020年5月20日）$ r $ $ 0.74 $，$ 95 \％$ $置信区间$ [0.48-0.86] $。
Timely estimation of the current value for COVID-19 reproduction factor $R$ has become a key aim of efforts to inform management strategies. $R$ is an important metric used by policy-makers in setting mitigation levels and is also important for accurate modelling of epidemic progression. This brief paper introduces a method for estimating $R$ from biased case testing data. Using testing data, rather than hospitalisation or death data, provides a much earlier metric along the symptomatic progression scale. This can be hugely important when fighting the exponential nature of an epidemic. We develop a practical estimator and apply it to Scottish case testing data to infer a current (20 May 2020) $R$ value of $0.74$ with $95\%$ confidence interval $[0.48 - 0.86]$.
