Chinese Clinical Trial Registry (Hong Kong), Hong Kong Chinese Medicine Clinical Study Centre, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China.
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
Chin J Integr Med. 2019 Dec;25(12):911-916. doi: 10.1007/s11655-018-3016-0. Epub 2018 Nov 22.
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.
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.
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.
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.
分析同期及前 1、2、3、4 年（中医“疫病三年后发”理论）气象因素与风疹发病率的相关性，建立北京市风疹气象预报模型。
收集北京市疾病预防控制中心 1990 年至 2004 年风疹发病率和北京市气象观测站 1986 年至 2004 年逐日平均气温、平均风速、平均降水量、平均相对湿度、平均蒸气压和平均低云量等气象变量数据。采用描述性统计和反向传播人工神经网络进行分析。
平均温度和相对湿度对风疹发病率有很大的影响（100%）。但其他气象因素的组合有助于提高风疹气象预报模型的准确性。利用当前和前 3 年的气象变量组合而不是利用单一年份或更早于 3 年前的数据，可以将预报精度提高 76%。
风疹发病率与当年及前 3 年的气象变量密切相关。这一发现表明，风疹预测可以受益于对以往气候变化的考虑。
