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

流行病学数据时间序列分析软件包的开发

基本信息

批准号：
60870090
负责人：
KIKKAWA Sho
金额：
$ 4.48万
依托单位：
TOKYO WOMEN'S MEDICAL COLLEGE
依托单位国家：
日本
项目类别：
Grant-in-Aid for Developmental Scientific Research
财政年份：
1985
资助国家：
日本
起止时间：
1985 至 1987
项目状态：
已结题

项目摘要

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.

彼此的表演数据和环境数据密切相互连接。因此，有必要同时分析它们，而不是独立，我们需要系统地收集和到达。由于此数据收集和布置是该项目最重要的部分，因此我们努力在流行病数据上向Maker Good Databae努力。我们收集了许多段落的每周数据，例如横滨，鲁贝拉（Rubeola）和一些疾病，例如东京的流感等，并将它们放入每个数据库中。我们还纠正了几年的一些自然环境数据，空气含量的contraion数据，流量量数据等。通过时间序列分析程序（例如，相关性，分析程序）多变量分析程序，分析了收集的数据，ETX。结果，已经知道了几种疾病的分类数量的某些统计特性，我们就某些dfiseases和环境因素之间的关系获得了一些互动发现。由于流行病学时间序列不一定是固定的，因此数据处理的理论研究通常很困难。因此，我们需要开发一些新的折磨。我们已经做出了预防，以开发新方法来分析非遗产时间。我们介绍了关于非组织过程的自由度数量的新想法，并表明这种自由度与熵的思想密切相关。此外，我们推导了有效带宽变化的时间，这是O时间序列的ninstationare频谱传播的定量露。 Banwidths播放Expientnat卷，以评估时间非常频谱的特性。