A Large Scale Spatio-temporal Binomial Regression Model for Estimating Seroprevalence Trends

TL;DR

This paper introduces a scalable Bayesian spatio-temporal binomial regression model to analyze large datasets of Lyme disease antibody prevalence, revealing regional trends and potential spread in the United States.

Contribution

It presents a novel scalable framework combining Gaussian processes and autoregressive models for large spatio-temporal data analysis.

Findings

Identified regions with increasing canine Lyme disease risk.

Evidence of worsening Lyme disease in endemic areas.

Potential spread to non-endemic regions.

Abstract

This paper develops a large-scale Bayesian spatio-temporal binomial regression model for the purpose of investigating regional trends in antibody prevalence to Borrelia burgdorferi, the causative agent of Lyme disease. The proposed model uses Gaussian predictive processes to estimate the spatially varying trends and a conditional autoregressive model to account for spatio-temporal dependence. Careful consideration is made to develop a novel framework that is scalable to large spatio-temporal data. The proposed model is used to analyze approximately 16 million Borrelia burgdorferi test results collected on dogs located throughout the conterminous United States over a sixty month period. This analysis identifies several regions of increasing canine risk. Specifically, this analysis reveals evidence that Lyme disease is getting worse in some endemic regions and that it could potentially be spreading to other non-endemic areas. Further, given the zoonotic nature of this vector-borne disease, this analysis could potentially reveal areas of increasing human risk.