BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies

Journal of Data Science

article

Shuang Jiang¹  Quan Zhou³  Xiaowei Zhan²  Qiwei Li⁴ 

[1] Department of Statistical Science, Southern Methodist University;Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center;Department of Statistics, Texas A&M University, College Station;Department of Mathematical Sciences, The University of Texas at Dallas

关键词: Bayesian hierarchical modeling;    multiple change-point detection;    Poisson segmented regression;    stochastic SIR model;   

DOI  :  10.6339/21-JDS1009

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

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 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) has the potential to adjust for any time-varying covariate effects. Our model can be used to evaluate public health interventions, identify latent events associated with spreading rates, and yield better short-term forecasts.

【 授权许可】

CC BY