ArticleBMC medical informatics and decision making2010
Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables.
Article in BMC medical informatics and decision making, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Estimated Incidence of Hospitalizations and Deaths Attributable to Respiratory Syncytial Virus Infections Among Adults in Germany Between 2015 and 2019.Infectious diseases and therapy · 2024Article
- Estimated Respiratory Syncytial Virus-Related Hospitalizations and Deaths Among Children and Adults in Spain, 2016-2019.Infectious diseases and therapy · 2024Article
- Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach.Epidemiologia (Basel, Switzerland) · 2021Article
- The Association between Weather Conditions and Admissions to the Paediatric Intensive Care Unit for Respiratory Syncytial Virus Bronchiolitis.Pathogens (Basel, Switzerland) · 2021Article
- A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate.Ecological modelling · 2020Article
- The impact of temperature and relative humidity on spatiotemporal patterns of infant bronchiolitis epidemics in the contiguous United States.Health & place · 2017Article
- Influence of meteorological conditions on RSV infection in Portugal.International journal of biometeorology · 2016Article
- Haze is a risk factor contributing to the rapid spread of respiratory syncytial virus in children.Environmental science and pollution research international · 2016Article
- Environmental drivers of the spatiotemporal dynamics of respiratory syncytial virus in the United States.PLoS pathogens · 2015Article
- A relationship between acute respiratory illnesses and weather.Epidemiology and infection · 2014Article
- Article
- Spatiotemporal patterns of infant bronchiolitis in a Tennessee Medicaid population.Spatial and spatio-temporal epidemiology · 2013Article
- Strengthening Partnerships along the Informatics Innovation Stages and Spaces: Research and Practice Collaboration in Utah.Online journal of public health informatics · 2011Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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