From Apple, New York, NY.
Google, Mountain View, CA.
Epidemiology. 2022 Jul 1;33(4):470-479. doi: 10.1097/EDE.0000000000001495. Epub 2022 May 10.
Accurate measurement of daily infection incidence is crucial to epidemic response. However, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating methods to remove the effects of stochastic delays from observed data. Existing estimators can be sensitive to model misspecification and censored observations; many analysts have instead used methods that exhibit strong bias. We develop an estimator with a regularization scheme to cope with stochastic delays, which we term the robust incidence deconvolution estimator. We compare the method to existing estimators in a simulation study, measuring accuracy in a variety of experimental conditions. We then use the method to study COVID-19 records in the United States, highlighting its stability in the face of misspecification and right censoring. To implement the robust incidence deconvolution estimator, we release incidental, a ready-to-use R implementation of our estimator that can aid ongoing efforts to monitor the COVID-19 pandemic.
准确测量每日感染发病率对于疫情应对至关重要。然而，症状出现、检测和报告的延迟掩盖了传播的动态，需要采用方法从观察数据中消除随机延迟的影响。现有的估计器可能对模型失拟和截尾观测敏感；许多分析人员转而使用具有强偏差的方法。我们开发了一种具有正则化方案的估计器来应对随机延迟，我们称之为稳健发病率反卷积估计器。我们在模拟研究中比较了该方法与现有估计器的性能，在各种实验条件下衡量了准确性。然后，我们使用该方法研究了美国的 COVID-19 记录，强调了它在面对模型失拟和右截断时的稳定性。为了实现稳健发病率反卷积估计器，我们发布了 incidental，这是我们的估计器的一个即用型 R 实现，可以帮助正在进行的监测 COVID-19 大流行的工作。
