Predicting the start week of respiratory syncytial virus outbreaks using real-time weather variables

Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreaks. Using readily available data, reliable prediction of the week these outbreaks will start could help pediatric hospitals better prepare for staffing and supplies. Naïve Bayes (NB) classifier models were constructed using weather data from 1985 to 2008 considering only variables that were available in real time and that could be used to forecast the week in which an RSV outbreak would occur in Salt Lake County, Utah (SLC). Outbreak start dates were documented by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008. NB models predicted RSV outbreaks up to three weeks in advance of the start date with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in this study were similar to those that lead to temperature inversions in the Salt Lake Valley. We demonstrate that Naïve Bayes classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.