论文标题
在有效繁殖数量的动态估计中过滤和改善的不确定性定量
Filtering and improved Uncertainty Quantification in the dynamic estimation of effective reproduction numbers
论文作者
论文摘要
有效的繁殖数量$ r_t $衡量感染病的传播性，因为在一个易感和不敏感宿主的人群中，一个繁殖时间中的二次感染数量。当前方法无法正确量化$ r_t $的不确定性，这是触发模式中观察到的可变性所预期的。我们通过改进Cori等人的泊松采样模型来详细阐述$ r_t $的贝叶斯估计。 （2013）。通过添加自动回归潜在过程，我们在观察到的$ r_t $ s的日志上构建了动态线性模型，从而导致过滤类型的贝叶斯推理。我们使用共轭分析，所有计算都是明确的。结果表明，对$ r_t $的估计的不确定性量化有所改善，并具有可靠的方法，可以通过非专家和其他预测系统安全地使用。我们用来自墨西哥当前COVID19流行病的最新数据来说明我们的方法。
The effective reproduction number $R_t$ measures an infectious disease's transmissibility as the number of secondary infections in one reproduction time in a population having both susceptible and non-susceptible hosts. Current approaches do not quantify the uncertainty correctly in estimating $R_t$, as expected by the observed variability in contagion patterns. We elaborate on the Bayesian estimation of $R_t$ by improving on the Poisson sampling model of Cori et al. (2013). By adding an autoregressive latent process, we build a Dynamic Linear Model on the log of observed $R_t$s, resulting in a filtering type Bayesian inference. We use a conjugate analysis, and all calculations are explicit. Results show an improved uncertainty quantification on the estimation of $R_t$'s, with a reliable method that could safely be used by non-experts and within other forecasting systems. We illustrate our approach with recent data from the current COVID19 epidemic in Mexico.
