medRxiv. 2021 Jan 18; doi: 10.1101/2020.10.06.20208132.

BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies.

medRxiv : the preprint server for health sciences

Shuang Jiang, Quan Zhou, Xiaowei Zhan, Qiwei Li

PMID: 33469604 PMCID: PMC7814850 DOI: 10.1101/2020.10.06.20208132

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 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.

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Publication Types

• Preprint

Grant support

• P30 CA142543/CA/NCI NIH HHS/United States
• R01 GM126479/GM/NIGMS NIH HHS/United States
• R01 HG008983/HG/NHGRI NIH HHS/United States