BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies

Jiang, Shuang; Zhou, Quan; Zhan, Xiaowei; Li, Qiwei

doi:10.6339/21-JDS1009

Journal of Data Science

BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies

Volume 19, Issue 3 (2021), pp. 365–389

Shuang Jiang Quan Zhou Xiaowei Zhan All authors (4)

https://doi.org/10.6339/21-JDS1009

Pub. online: 24 February 2021 Type: Statistical Data Science

Received
7 October 2020

Accepted
4 February 2021

Published
24 February 2021

Abstract

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.

Supplementary material

Supplementary Material

Supplement A: Software. We provide software in the form of R/C++ codes on GitHub https://github.com/shuangj00/BayesSMILES. We have designed a website https://shuangj00.github.io/BayesSMILES/ to summarize the inference results for the 50 U.S. states, as a supplement to Section 6. The website shows that 1) the detected change points for each U.S. state; and 2) the COVID-19 transmission dynamics based on the segment-varying basic reproduction numbers R 0 ’s, including their posterior means and 95 % credible intervals.Supplement B: Supplementary document. The supplementary file encloses the detailed Markov chain Monte Carlo algorithms, additional simulation study and real data analysis results, and key notation tables.

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Keywords

Bayesian hierarchical modeling multiple change-point detection Poisson segmented regression stochastic SIR model

Funding

This work was supported by the University of Texas at Dallas (UT Dallas) Office of Research [UT Dallas Center for Disease Dynamics and Statistics] and partially supported by the National Institutes of Health [1R56HG011035, 5P30CA142543, 5R01GM126479, 5R01HG008983].

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