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

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.

The impact of temperature and relative humidity on spatiotemporal patterns of infant bronchiolitis epidemics in the contiguous United States

“…RSV circulation follows distinct annual patterns similar to influenza, peaking primarily between November and March in the northern hemisphere and during April and September in the southern hemisphere (Noyola and Mandeville 2008; Taylor et al 2016; Walton et al 2010; Welliver Sr 2007). In the tropics, RSV circulation typically corresponds with the rainy season, though the seasonal peak is not pronounced (Chan et al 2002).…”

Respiratory syncytial virus. Clinical and epidemiological pattern in pediatric patients admitted to a children’s hospital between 2000 and 2013

“…1 Such biannual cyclic pattern was first described in Europe; in our 14-year study, this alternation was corroborated in the past five years, similar to what has been described by different studies. 26,27 The value of these data is still limited, but if cycles were confirmed to be regular, data would become vitally significant for planning health actions. 1 The largest RSV outbreak occurred in 2003, although major outbreaks were also observed in 2010 and 2012 with an early onset, alternating with outbreaks of lesser magnitude and later onset in other years.…”

A systematic review of predictive modeling for bronchiolitis

“…We can consider certain chronic conditions, such as congenital heart disease, chronic lung disease, and severe neurological diseases, which may increase the risk of RSV hospitalization, but are ignored in existing predictive models [144]. We can make prediction on a monthly basis, in a way similar to that in Rietveld et al [33], accounting for community viral prevalence, prevailing weather patterns [183][184][185], etc.…”

“…Along this direction, Walton et al [184] built a naive Bayes model to predict the onset of community RSV activity up to three weeks in advance, with a sensitivity of 67% and a specificity of 94%. The model's prediction accuracy is inadequate for practical use [185].…”