Influenza Early Warning Model Based on Yunqi Theory

Objective：To establish an early warning model to simulate the outbreak of influenza based on weather conditions and Yunqi theory,an ancient calendar theory of Chinese medicine（CM）.Methods：Tianjin, a northeastern city in China,was chosen as the region of research and applied the influenza-like illness attack rate（ILI）%as the baseline and warning line to determine the severity of influenza epidemic.Then,an influenza early warning model was constructed based on the theory of rough set and support vector machines（RS-SVM）, and the relationship between influenza and meteorology was explored through analyzing the monitoring data. Results：The predictive performance of the model was good,which had achieved 81.8%accuracy when grouping the obtained data into three levels that represent no danger,danger of a light epidemic,and danger of a severe epidemic.The test results showed that if the host qi and guest qi were not balanced,this kind of situation was more likely to cause influenza outbreaks.Conclusions：The outbreak of influenza closely relates to temperature, humidity,visibility,and wind speed and is consistent with some part of CM doctrine.The result also indicates that there is some reasonable evidence in the Yunqi theory.

Correlation Analysis for the Attack of Bacillary Dysentery and Meteorological Factors Based on the Chinese Medicine Theory of Yunqi and the Medical-Meteorological Forecast Model

Objective： To explore the impact of meteorological factors on the outbreak of bacillary dysentery, so as to provide suggestions for disease prevention. Methods： Based on the Chinese medicine theory of Yunqi, the descriptive statistics, single-factor correlation analysis and back-propagation artificial neural net-work were conducted using data on five basic meteorological factors and data on incidence of bacillary dysentery in Beijing, China, for the period 1970-2004. Results： The incidence of bacillary dysentery showed significant positive correlation relationship with the precipitation, relative humidity, vapor pressure, and temperature, respectively. The incidence of bacillary dysentery showed a negatively correlated relationship with the wind speed and the change trend of average wind speed. The results of medical-meteorological forecast model showed a relatively high accuracy rate. Conclusions： There is a close relationship between the meteorological factors and the incidence of bacillary dysentery, but the contributions of which to the onset of bacillary dysentery are different to each other.