BAYESIAN LEARNING OF COVID-19 VACCINE SAFETY WHILE INCORPORATING ADVERSE EVENTS ONTOLOGY.

ABSTRACT: While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false positives. We propose a Bayesian graph-assisted signal selection (BGrass) model to simultaneously estimate all AEs while incorporating the network of dependence between AEs. Under a fully Bayesian

SUBMITTER: Zhao B 

PROVIDER: S-EPMC11395664 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature