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.

Estimating seasonal onsets and peaks of bronchiolitis with spatially and temporally uncertain data.

RSV bronchiolitis (an acute lower respiratory tract viral infection in infants) is the most common cause of infant hospitalizations in the United States (US). The only preventive intervention currently available is monthly injections of immunoprophylaxis. However, this treatment is expensive and needs to be administered simultaneously with seasonal bronchiolitis cycles in order to be effective. To increase our understanding of bronchiolitis timing, this research focuses on identifying seasonal bronchiolitis cycles (start times, peaks, and declinations) throughout the continental US using data on infant bronchiolitis cases from the US Military Health System Data Repository. Because this data involved highly personal information, the bronchiolitis dates in the dataset were "jittered" in the sense that the recorded dates were randomized within a time window of the true date. Hence, we develop a statistical change point model that estimates spatially varying seasonal bronchiolitis cycles while accounting for the purposefully introduced jittering in the data. Additionally, by including temperature and humidity data as regressors, we identify a relationship between bronchiolitis seasonality and climate. We found that, in general, bronchiolitis seasons begin earlier and are longer in the southeastern states compared to the western states with peak times lasting approximately 1 month nationwide.

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.

Comparison of seclusion and restraint prevalence between hearing patients and deaf or hard of hearing patients in a state hospital setting.

The present study is an effort to obtain preliminary data to assess the validity of the long-standing claim that the rate of seclusion and restraint is higher among deaf and hard of hearing individuals than among hearing individuals. This difference has been claimed repeatedly despite there being no research to support it. The sample was comprised of 22 deaf or hard of hearing individuals who had been committed to a large state hospital, all but three of whom had been discharged prior to data collection. The deaf and hard of hearing subjects were matched to subjects with no hearing loss on factors believed to be associated with behaviors that can result in seclusion or restraint. Archived clinical records of both groups of subjects were reviewed to determine the rates of seclusion and restraint for the two groups, as well as to assess the length of time each group was in seclusion or restraint. The results indicate a significantly higher frequency of seclusion and restraint for the deaf and hard of hearing group than for the hearing group. Of note is that the hearing individuals spent longer in seclusion and restraint than did the deaf and hard of hearing. The hypothesis of a higher rate of restrictive events among deaf and hard of hearing individuals is supported. The results of this study suggest that further research be undertaken to determine the generalizability of these results as well as possible sources of the differences between these two groups.