论文标题
使用活动案例，一种新的估算方法，用于共同变化的繁殖数
A new estimation method for COVID-19 time-varying reproduction number using active cases
论文作者
论文摘要
我们提出了一种新方法，以估计新型冠状病毒病（COVID-19）的时变有效（或瞬时）繁殖数。该方法基于描述病毒传播的离散时间随机增强室模型。一种两阶段估计方法，结合了扩展的卡尔曼滤波器（EKF），以估算报告的状态变量（活动和删除的情况）和基于合理转移函数的低通滤波器，以消除报告病例的短期波动，与假定遵循高斯分布的病例不确定性一起使用。我们的方法不需要有关串行间隔的信息，这使得估计程序在不降低估计质量的情况下变得更加简单。我们表明，所提出的方法可与常见方法相媲美，例如，基于年龄结构和基于新病例的顺序贝叶斯模型。我们还将其应用于斯堪的纳维亚国家的COVID-19案件：丹麦，瑞典和挪威，在那里，正人率低于5 \％。
We propose a new method to estimate the time-varying effective (or instantaneous) reproduction number of the novel coronavirus disease (COVID-19). The method is based on a discrete-time stochastic augmented compartmental model that describes the virus transmission. A two-stage estimation method, which combines the Extended Kalman Filter (EKF) to estimate the reported state variables (active and removed cases) and a low pass filter based on a rational transfer function to remove short term fluctuations of the reported cases, is used with case uncertainties that are assumed to follow a Gaussian distribution. Our method does not require information regarding serial intervals, which makes the estimation procedure simpler without reducing the quality of the estimate. We show that the proposed method is comparable to common approaches, e.g., age-structured and new cases based sequential Bayesian models. We also apply it to COVID-19 cases in the Scandinavian countries: Denmark, Sweden, and Norway, where the positive rates were below 5\% recommended by WHO.
