Jiang Shuang, Zhou Quan, Zhan Xiaowei, Li Qiwei
Department of Statistical Science, Southern Methodist University, Dallas, TX 75205, USA.
Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
medRxiv. 2021 Jan 18:2020.10.06.20208132. doi: 10.1101/2020.10.06.20208132.
The coronavirus disease of 2019 (COVID-19) is a pandemic. To characterize its disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model builds on a Bayesian Poisson segmented regression model that can 1) capture the epidemiological dynamics under the changing conditions caused by external or internal factors; 2) provide uncertainty estimates of both the number and locations of change points; and 3) adjust any explanatory time-varying covariates. Our model can be used to evaluate public health interventions, identify latent events associated with spreading rates, and yield better short-term forecasts.
2019冠状病毒病（COVID-19）是一种大流行病。为了描述其疾病传播性，我们提出了一种使用每日活跃感染病例的贝叶斯变化点检测模型。我们的模型基于贝叶斯泊松分段回归模型构建，该模型能够：1）捕捉由外部或内部因素引起的变化条件下的流行病学动态；2）提供变化点数量和位置的不确定性估计；3）调整任何随时间变化的解释性协变量。我们的模型可用于评估公共卫生干预措施、识别与传播率相关的潜在事件，并做出更好的短期预测。
