BACKGROUND: Information on the onset of epidemics of acute respiratory tract infections (ARIs) is useful in timing preventive strategies (eg, the passive immunization of high-risk infants against respiratory syncytial virus [RSV]). Aiming at better predictions of the seasonal activity of ARI pathogens, we investigated the influence of climate on hospitalizations for ARIs. METHODS: Samples obtained from 3044 children hospitalized with ARIs in Mainz, Germany, were tested for pathogens with a multiplex reverse-transcriptase polymerase chain reaction enzyme-linked immunosorbent assay from 2001 through 2006. Hospitalizations for ARIs were correlated with meteorological parameters recorded at the University of Mainz. The frequency of hospitalization for RSV infection was predicted on the basis of multiple time series analysis. RESULTS: Influenza A, RSV, and adenovirus were correlated with temperature and rhinovirus to relative humidity. In a time series model that included seasonal and climatic conditions, RSV-associated hospitalizations were predictable. CONCLUSIONS: Seasonality of certain ARI pathogens can be explained by meteorological influences. The model presented herein is a first step toward predicting annual RSV epidemics using weather forecast data.
BACKGROUND: Information on the onset of epidemics of acute respiratory tract infections (ARIs) is useful in timing preventive strategies (eg, the passive immunization of high-risk infants against respiratory syncytial virus [RSV]). Aiming at better predictions of the seasonal activity of ARI pathogens, we investigated the influence of climate on hospitalizations for ARIs. METHODS: Samples obtained from 3044 children hospitalized with ARIs in Mainz, Germany, were tested for pathogens with a multiplex reverse-transcriptase polymerase chain reaction enzyme-linked immunosorbent assay from 2001 through 2006. Hospitalizations for ARIs were correlated with meteorological parameters recorded at the University of Mainz. The frequency of hospitalization for RSV infection was predicted on the basis of multiple time series analysis. RESULTS: Influenza A, RSV, and adenovirus were correlated with temperature and rhinovirus to relative humidity. In a time series model that included seasonal and climatic conditions, RSV-associated hospitalizations were predictable. CONCLUSIONS: Seasonality of certain ARI pathogens can be explained by meteorological influences. The model presented herein is a first step toward predicting annual RSV epidemics using weather forecast data.
Authors: Nephi A Walton; Mollie R Poynton; Per H Gesteland; Chris Maloney; Catherine Staes; Julio C Facelli Journal: BMC Med Inform Decis Mak Date: 2010-11-02 Impact factor: 2.796
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