Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana
Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana
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DOI：
10.1080/2330443x.2020.1859030
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发表时间：
2021-02-04
影响因子：
1.6
通讯作者：
Sledge, Daniel
中科院分区：
文献类型：
--
作者：
Mohler, George;Short, Martin B.;Sledge, Daniel
对新冠肺炎繁殖量的动态估计对于评估公共卫生措施对病毒传播的影响非常重要。州和地方是否放松或加强缓解措施的决定在一定程度上是基于复制数量R-t是否低于自我维持的值1。使用分支点过程模型和来自印第安纳州的新冠肺炎数据作为案例研究，我们表明，对R-t的现值的估计，以及它是高于还是低于1，关键取决于对数据选择以及模型规范和估计的选择。特别是，当我们改变估计量和输入数据集的类型时，我们发现R-t值的范围从0.47到1.20。我们提出了模型比较和评估的方法，然后讨论了我们的发现的政策含义。
Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, R-t, falls below the self-sustaining value of 1. Employing branching point process models and COVID-19 data from Indiana as a case study, we show that estimates of the current value of R-t, and whether it is above or below 1, depend critically on choices about data selection and model specification and estimation. In particular, we find a range of R-t values from 0.47 to 1.20 as we vary the type of estimator and input dataset. We present methods for model comparison and evaluation and then discuss the policy implications of our findings.