ABSTRACT

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.

摘要

动态评估COVID-19的繁殖数量对于评估公共卫生措施对病毒传播的影响非常重要。是否放宽或加强缓解措施的州和地方决策部分是根据繁殖数量R _t 是否低于自我维持值1来决定的。采用分支点过程模型和印第安纳州的COVID-19数据作为案例研究表明，R _t 的当前值的估计值，无论是大于还是小于1，都主要取决于数据选择，模型规格和估计的选择。特别是，我们发现R _t的范围 值从0.47到1.20，这是因为我们更改了估算器和输入数据集的类型。我们介绍了模型比较和评估的方法，然后讨论了我们发现的政策含义。