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

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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.