BOST ‐ LAWS : A Bayesian Online Spatio‐Temporal Prediction Framework With Likelihood‐Adjusted Weighted Smoothing for Disease Surveillance

ABSTRACT Accurate prediction of disease transmission is challenged by its dynamic nature, influenced by factors such as population density. This study introduces a novel approach that enhances predictions of disease surveillance data by integrating likelihood weighting into the integrated nested Laplace approximation (INLA) framework, specifically tailored to account for population density within a spatiotemporal Bayesian methodology. Our method prioritizes recent information for non‐stationary outbreak time series online prediction by employing calibrated discounting on historical data through weight adjustments on their likelihoods. Empirical analysis of real COVID‐19 daily case count data from Massachusetts counties demonstrates the effectiveness of this approach, revealing improved prediction accuracy compared to existing methods. The INLA‐based method with weighted smoothing offers a promising avenue for enhancing infectious disease forecasting models, with significant potential applications in public health decision‐making and resource allocation.