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Abstract
BackgroundRespiratory 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.MethodsNaïve 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.ResultsNB 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.ConclusionsWe 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.
Highlights
Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons
As suggested by the mathematical models, the different patterns observed for high and low seasons were due to the size of the susceptible population, it would be expected that on a low outbreak season there would be a greater lag between outbreak stimulus and the exponential growth of confirmed RSV cases
We selected records for patients that reside in Salt Lake County, Utah that meet the following criteria: 1) laboratory confirmation of RSV by viral culture or direct fluorescent antibody (DFA) or/and polymerase chain reaction (PCR); or 2) a discharge diagnosis coded with a bronchiolitis or RSV-related ICD-9 code, including: 466 acute bronchitis and bronchiolitis; 466.1 acute bronchiolitis; 466.11 acute bronchiolitis due to respiratory syncytial virus (RSV); and 466.19 acute bronchiolitis due to other infectious organism
Summary
Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. RSV has a characteristic biennial outbreak pattern with alternating low peak and high peak seasons [2] While exceptions of this biennial pattern have been reported in the literature [7,8,9,10,11] we observed the normal biennial pattern in all the data used in this study. It has been shown using mathematical models that this pattern of high peak and low peak outbreaks can be explained by the variation in the number of susceptible individuals in the population, which is considerably diminished following a large outbreak [1,4,12]. It is important to develop independent models for high peak and low peak years
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