论文标题
在存在观察偏见的情况下，有症状的情况的$ r_t $的系统错误估计为$ r_t $
Systematic errors in estimates of $R_t$ from symptomatic cases in the presence of observation bias
论文作者
论文摘要
我们考虑了估计流行病的繁殖数量$ r_t $的问题，因为案件检测的可能性取决于已知的协变量。我们认为，在这种情况下，正常的经验估计量可能会在群体之间的患病率随时间变化时失败。我们提出了解决问题的贝叶斯策略，以及大量案例的简单解决方案。我们在一项简单但现实的仿真研究中说明了问题及其解决方案，并讨论了该问题与当前Covid19大流行的一般相关性。
We consider the problem of estimating the reproduction number $R_t$ of an epidemic for populations where the probability of detection of cases depends on a known covariate. We argue that in such cases the normal empirical estimator can fail when the prevalence of cases among groups changes with time. We propose a Bayesian strategy to resolve the problem, as well as a simple solution in the case of large number of cases. We illustrate the issue and its solution on a simple yet realistic simulation study, and discuss the general relevance of the issue to the current covid19 pandemic.
