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  • 标题:Better Management of Rabies Using Innovative Statistical Tools
  • 本地全文:下载
  • 作者:Andreea MIRICA ; Octavian CEBAN ; Georgiana Andreea FERARIU
  • 期刊名称:Communications of the IBIMA
  • 电子版ISSN:1943-7765
  • 出版年度:2020
  • 卷号:2020
  • 页码:1-9
  • DOI:10.5171/2020.537282
  • 出版社:IBIMA Publishing
  • 摘要:The aim of this paper is to illustrate how statistical tools can benefit healthcare policy makers specialised in the rabies management. Understanding the prevalence of such a disease is crucial for management teams. Seasonality may be a salient feature of data series provided by medical units, that is why the use of an innovative tool for seasonal adjustment is needed. For this paper, the data series (number of persons examined for rabies, by age and residency) were provided by one single health care unit – The National Institute of Infectious Diseases “Matei Bals” . Methodologically, the graphs of both series were analyzed in order to indicate the type of transformation needed: seasonality test and outlier detection. When seasonality is detected, the data series were seasonally adjusted using JDemetra 2.2.2, a software recommended by Eurostat, which incorporates TRAMO-SEATS and X13 procedure specifications. Following the Hungarian Central Statistics Office’s guidelines (2007), if the seasonal variation is not changing along with the trend, then no transformation is needed; however, if the seasonal variation is changing along with the trend, a log-transformation is necessary. The results of the analysis show that all the series display clear seasonal patterns and that should be log-transformed prior to seasonal adjustment. The only series that displayed outliers is the number of persons examined for rabies from Bucharest. JDemetra offers not only seasonality tests and an automatic procedure for seasonal adjustment, but also a very useful tool for policy makers; the outlier detection tool, which could be used to evaluate different policies in time.
  • 关键词:seasonality adjusted data; outlier detection; JDemetra ; log transformation
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