A Bayesian approach to real-time spatiotemporal prediction systems for bronchiolitis.

Respiratory Syncytial Virus (RSV) induced bronchiolitis is a common lung infection and a major cause of infant hospitalization and mortality. Unfortunately, there is no known cure for RSV but several vaccines are in various stages of clinical trials. Currently, immunoprophylaxis is a preventative measure consisting of a series of monthly shots that should be administered at the start, and throughout, peak RSV season. Thus, the successful implementation of immunoprophylaxis is contingent upon understanding when outbreak seasons will begin, peak, and end. In this research we estimate the seasonal epidemic curves of RSV induced bronchiolitis using a spatially varying change point model. Further, in a novel approach and using the fitted change point model, we develop a historical matching algorithm to generate real time predictions of seasonal curves for future years.

Nonparametric Tree-Based Predictive Modeling of Storm Outages on an Electric Distribution Network.

This article compares two nonparametric tree-based models, quantile regression forests (QRF) and Bayesian additive regression trees (BART), for predicting storm outages on an electric distribution network in Connecticut, USA. We evaluated point estimates and prediction intervals of outage predictions for both models using high-resolution weather, infrastructure, and land use data for 89 storm events (including hurricanes, blizzards, and thunderstorms). We found that spatially BART predicted more accurate point estimates than QRF. However, QRF produced better prediction intervals for high spatial resolutions (2-km grid cells and towns), while BART predictions aggregated to coarser resolutions (divisions and service territory) more effectively. We also found that the predictive accuracy was dependent on the season (e.g., tree-leaf condition, storm characteristics), and that the predictions were most accurate for winter storms. Given the merits of each individual model, we suggest that BART and QRF be implemented together to show the complete picture of a storm's potential impact on the electric distribution network, which would allow for a utility to make better decisions about allocating prestorm resources.