Bayesian Online Spatiotemporal Disease Detection with Likelihood-Weighted Smoothing

Music City Center 

The dynamic nature of disease transmission, influenced by factors like population density, poses a significant challenge to accurate prediction. The study introduces a novel approach, integrating likelihood weighting into Integrated Nested Laplace Approximation (INLA) based on population density, to predict disease surveillance data through spatio-temporal Bayesian methodology. For non-stationary outbreak time series online prediction, our approach prioritizes accounting for more recent information with calibrated discounting on old information through weight adjustment on their likelihood. Empirical analysis on real COVID-19 daily case count data in Massachusetts counties demonstrates the effectiveness of our approach, showing improved prediction accuracy compared to existing methods. Our INLA-based method with weighted smoothing presents a promising avenue for enhancing infectious disease forecasting models, with potential applications in public health decision-making and resource allocation.

Likelihood-Weighting

Spatiotemporal

INLA

Bayesian 

Section on Bayesian Statistical Science