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Activity Number: 64 - Computational Advances in Bayesian Inference
Type: Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #323902
Title: A Zero-Inflated Conway--Maxwell--Poisson Regression Model with Spatially-Varying Dispersion for Spatiotemporal Data of US Vaccine Refusal
Author(s): Bokgyeong Kang* and John Hughes and Shweta Bansal and Murali Haran
Companies: The Pennsylvania State University and Lehigh University and Georgetown University and Pennsylvania State University

Vaccination is widely recognized as one of the most effective tools for preventing disease. However, parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. Our aim is to identify demographic or socioeconomic characteristics associated with vaccine refusal, which could help public health professionals and medical providers develop interventions targeted to concerned parents. We examine US county-level vaccine refusal data for patients under five years of age collected on a monthly basis during the period 2012--2015. These data exhibit challenging features: zero inflation, spatially-varying dispersion, spatial dependence, and large sample size (over 3,000 counties). We propose a zero-inflated Conway--Maxwell--Poisson regression model that addresses these challenges. We use the asymptotically exact exchange algorithm and the sampling technique proposed by Chanialidis et al. (2018) to do Bayesian inference for our model. Our Bayesian framework permits efficient sampling and provides asymptotically exact estimates.

Authors who are presenting talks have a * after their name.

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