论文标题
测定大规模测试模型来解释COVID-19案例编号
Assaying Large-scale Testing Models to Interpret COVID-19 Case Numbers
论文作者
论文摘要
大规模测试被认为是评估当前COVID-19大流行状态的关键。然而，报告的病例数与大流行的真实状态之间的联系仍然难以捉摸。我们基于有关此链接的竞争假设开发数学模型，从而根据病例数提供了不同的患病率估计，并通过预测SARS-COV-2征收的死亡率轨迹来验证它们。假设个人仅根据具有传染性的预定风险进行测试，则意味着绝对病例数反映了流行率，但事实证明是一个差的预测指标，在两个Covid-19-19-19-19-19-19流行病开始时始终高估了生长速率。相比之下，假设测试能力得到完全利用会表现更好。这导致将百分比阳性速率用作更强大的流行动力学指标，但是我们发现，随着测试数量的较大，它需要考虑到饱和现象。
Large-scale testing is considered key to assess the state of the current COVID-19 pandemic. Yet, the link between the reported case numbers and the true state of the pandemic remains elusive. We develop mathematical models based on competing hypotheses regarding this link, thereby providing different prevalence estimates based on case numbers, and validate them by predicting SARS-CoV-2-attributed death rate trajectories. Assuming that individuals were tested based solely on a predefined risk of being infectious implies the absolute case numbers reflect the prevalence, but turned out to be a poor predictor, consistently overestimating growth rates at the beginning of two COVID-19 epidemic waves. In contrast, assuming that testing capacity is fully exploited performs better. This leads to using the percent-positive rate as a more robust indicator of epidemic dynamics, however we find it is subject to a saturation phenomenon that needs to be accounted for as the number of tests becomes larger.
