Department of Computer Science and Engineering, The Ohio State University, United States of America.
Department of Computer Science and Engineering, The Ohio State University, United States of America.
Math Biosci. 2022 Jan;343:108677. doi: 10.1016/j.mbs.2021.108677. Epub 2021 Nov 27.
Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID-19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting.
由于报告延迟，每日全国和全州的 COVID-19 发病率计数往往不可靠，需要根据最近的数据进行估计。这个过程在经济学中被称为实时预测。我们在本文中描述了一种简单的随机森林统计模型，用于根据历史数据以及一组简单的协变量（例如当前报告的感染计数、星期几和首次报告后的时间）对 COVID-19 每日新感染计数进行实时预测。我们将该模型应用于调整俄亥俄州的每日感染计数，并表明该简单的数据驱动方法的预测结果在质量和计算负担方面均优于采用复杂统计算法的最先进的分层贝叶斯模型的预测结果。执行实时预测的交互式笔记本可在 https://tinyurl.com/simpleMLnowcasting 在线获取。
