DEVELOPEMTN OF A TIME-SERIES ANALYSES SOFTWARE PACKAGE FOR EPIDENIOLOGIC DATA

流行病学数据和环境数据密切相关。因此，需要同时分析，而不是单独分析，需要系统地收集和整理。由于这个数据的收集和整理是这个项目最重要的部分，我们努力做好流行病学数据的数据库。我们每周收集许多疾病的数据，例如横滨的哮喘，风疹和东京的流感等疾病，并将它们放入每个数据库。我们还对一些多年的自然环境数据、空气污染物浓度数据、交通量数据等进行了校正。收集到的数据用时间序列分析程序进行分析，如相关性分析程序、多变量分析程序等。结果，我们了解了几种疾病患者数量的一些统计特性，并在一些疾病与环境因素的关系上获得了一些有趣的发现。由于流行病学时间序列不一定是平稳的，数据处理的理论研究往往是困难的。因此，我们需要发展一些新的理论。我们一直在努力开发分析非平稳时间序列的新方法。我们引入了关于非平稳过程的自由度数的新概念，并证明了这个自由度数与熵的概念密切相关。进一步，我们推导了时变有效带宽，它是时间序列非平稳谱扩频的定量度量。带宽对实时频谱性能的评价起着重要的作用。

Epidemilogic data and environmental data are closely connected with each other. Therefore, it is necessary to analyze them simultaneouslt, not independently, and we need to collect and arrage them systematically. Since this data collection and arrangement are the most important part of the project, we have endeavored to maker good databae on epidemiolgic data. We collected weekly data of many disseses, for example, asthma in Yokohama, rubeola and some disease like influenza in Tokyo, etc., and put them into each database. We also corrected some natural environmental data of several years, air contaninant concetraion data, traffic volume data, etc. Collected data were analyzed with time series analysis programs, for example, correlation, analysis programs multivariate analysis programs, etx. As a result, some statistical properties of the number of partients of several diseases were known and we obtained some intersting findings on the relation between some dfiseases and environmental factors. Since epidemiologic time series is not necessarily stationary, the theoretical study of the data processing is often difficult. Therefore, we need to develop some new thories. We have made efforets to develop new methods to analyze a nonstatinary timesseries. We introduced new idea on the number of degrees of freedom of a nonstationary process and showed that this number of degrees of freedom is closely connected with the idea of ENTROPY. Further, we deduced time varying effective bandwidths which are quantitative neasures of the spread of ninstationary spectrum of o the time series. The banwidths play importnat rolls for evaluation of the property of the time verying spectrum.