Open Access Open Badges Research article

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

Nephi A Walton1, Mollie R Poynton12, Per H Gesteland13, Chris Maloney13, Catherine Staes1 and Julio C Facelli14*

Author Affiliations

1 Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA

2 College of Nursing, University of Utah, Salt Lake City, Utah, USA

3 Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA

4 Center for High Performance Computing, University of Utah, Salt Lake City, Utah, USA

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BMC Medical Informatics and Decision Making 2010, 10:68  doi:10.1186/1472-6947-10-68

Published: 2 November 2010



Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.


Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined 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 3 weeks in advance 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 our study were similar to those that lead to temperature inversions in the Salt Lake Valley.


We demonstrate that Naïve Bayes (NB) 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.