论文标题
Covid-19阳性率的贝叶斯建模 - 印第安纳州的体验
Bayesian Modeling of COVID-19 Positivity Rate -- the Indiana experience
论文作者
论文摘要
在这份简短的技术报告中，我们在贝叶斯框架内模拟了印第安纳州报告的阳性测试速率，这还考虑了执行测试的每日计数中的实质性变异性（和过度的）。我们采用的方法，具有简单的预测过程，即后验，以这种“阳性”的速度，并允许任何代理商对COVID-19测试的每日结果进行简单而直接的适应。本文提供的数值结果是通过可更新的R标记文档获得的。
In this short technical report we model, within the Bayesian framework, the rate of positive tests reported by the the State of Indiana, accounting also for the substantial variability (and overdispeartion) in the daily count of the tests performed. The approach we take, results with a simple procedure for prediction, a posteriori, of this rate of 'positivity' and allows for an easy and a straightforward adaptation by any agency tracking daily results of COVID-19 tests. The numerical results provided herein were obtained via an updatable R Markdown document.
