摘要
Objective: To analyze the correlations between the incidence of rubella and meteorological factors over the same period and previous periods including 1, 2, 3 and 4 year ago(defined according to Chinese medicine Yunqi theory of "pestilence occurring after 3 years") and establish the rubella-meteorological forecast models for Beijing area, China. Methods: Data regarding the incidence of rubella between 1990 and 2004 from Beijing Center for Disease Control and Prevention, and the meteorological variables including daily average temperatures, daily average wind speeds, average precipitations, average relative humidity, average vapor pressures and average low cloud covers between 1986 and 2004 were collected from the Beijing Meteorological Observatory. Descriptive statistics and back-propagation artificial neural network for forecast model’s establishment were adopted for data analysis. Results: The average temperature and relative humidity have a great contribution(100%) to the rubella morbidity. But the combination of other meteorological factors contributed to improve the accuracy of rubella-meteorological forecast models. The forecast accuracy could be improved by 76% through utilizing a combination of meteorological variables spanning from 3 years ago to the present rather than utilizing data from a single year or dating back to more earlier time than 3 years. Conclusions: There is a close relationship between the incidence of rubella and meteorological variables in current year and previous 3 years. This finding suggests that rubella prediction would benefit from consideration to previous climate changes.
Objective: To analyze the correlations between the incidence of rubella and meteorological factors over the same period and previous periods including 1, 2, 3 and 4 year ago(defined according to Chinese medicine Yunqi theory of "pestilence occurring after 3 years") and establish the rubella-meteorological forecast models for Beijing area, China. Methods: Data regarding the incidence of rubella between 1990 and 2004 from Beijing Center for Disease Control and Prevention, and the meteorological variables including daily average temperatures, daily average wind speeds, average precipitations, average relative humidity, average vapor pressures and average low cloud covers between 1986 and 2004 were collected from the Beijing Meteorological Observatory. Descriptive statistics and back-propagation artificial neural network for forecast model’s establishment were adopted for data analysis. Results: The average temperature and relative humidity have a great contribution(100%) to the rubella morbidity. But the combination of other meteorological factors contributed to improve the accuracy of rubella-meteorological forecast models. The forecast accuracy could be improved by 76% through utilizing a combination of meteorological variables spanning from 3 years ago to the present rather than utilizing data from a single year or dating back to more earlier time than 3 years. Conclusions: There is a close relationship between the incidence of rubella and meteorological variables in current year and previous 3 years. This finding suggests that rubella prediction would benefit from consideration to previous climate changes.
基金
Supported by the National Natural Science Foundation of China(No.81072896)
Young Scientists Fund of the National Natural Science Foundation of China(No.81704198)
