论文标题
从行为数据中对COVID-19的有效繁殖数的实时估计
Real-time estimation of the effective reproduction number of COVID-19 from behavioral data
论文作者
论文摘要
对有效繁殖数量的近乎真实的时间估计是跟踪大流行并告知政策制定者和公众的最重要工具之一。但是，这些估计取决于报告的病例数，这些病例数通常以明显的偏见记录。流行病的结果受到社会接触的动态的强烈影响，社会接触的动力学在常规监视系统中被忽略，因为它们的实时观察具有挑战性。在这里，我们提出了一个使用在线和离线行为数据的概念，以每天的速度记录年龄分层的接触矩阵。使用重建的矩阵对流行病进行建模，我们在匈牙利的COVID-19大流行的两个第一波中动态估计有效的繁殖数。我们的结果表明，如何使用行为数据来构建替代性监测系统，以补充既定的公共卫生监视。当官方估计因观察性偏见而显得不可靠时，他们可以识别并提供更好的信号。
Near-real time estimations of the effective reproduction number are among the most important tools to track the progression of a pandemic and to inform policy makers and the general public. However, these estimations rely on reported case numbers, commonly recorded with significant biases. The epidemic outcome is strongly influenced by the dynamics of social contacts, which are neglected in conventional surveillance systems as their real-time observation is challenging. Here, we propose a concept using online and offline behavioral data, recording age-stratified contact matrices at a daily rate. Modeling the epidemic using the reconstructed matrices we dynamically estimate the effective reproduction number during the two first waves of the COVID-19 pandemic in Hungary. Our results demonstrate how behavioral data can be used to build alternative monitoring systems complementing the established public health surveillance. They can identify and provide better signals during periods when official estimates appear unreliable due to observational biases.
