EPIPOI: A user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series
EPIPOI: A user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series
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DOI：
10.1186/1471-2458-12-982
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发表时间：
2012-11-15
影响因子：
4.5
通讯作者：
McCormick, Benjamin J. J.
中科院分区：
文献类型：
--
作者：
Alonso, Wladimir J.;McCormick, Benjamin J. J.
背景资料：随着时间的推移，越来越需要处理和理解系统收集公共卫生数据所产生的相关信息。然而，对这些时间序列的分析通常需要先进的建模技术，而从事公共卫生和流行病学工作的工作人员、技术人员和研究人员不一定掌握这些技术。在这里，一个用户友好的工具，EPIPOI，有利于探索和提取参数描述的趋势，季节性和异常的特点流行病学过程。它还可以跨地理区域检查这些参数。虽然可视化的探索和提取相关参数的时间序列数据是至关重要的流行病学研究，到现在为止，它在很大程度上被限制到specialists.Methods：EPIPOI是免费提供的软件开发的Matlab（Mathworks公司），运行在PC和Mac计算机上。其友好的界面引导用户直观地通过有用的比较分析，包括时间parameters.Results的空间模式的比较：EPIPOI是能够处理复杂的分析，在一个方便的方式。一个原型已经被用来帮助研究人员在各种情况下，从公共卫生研讨会的教学使用的主要分析工具，在已发表的research.Conclusions：EPIPOI可以帮助公共卫生官员和学生探索时间序列数据使用广泛的复杂的分析和可视化工具。它还提供了一个分析环境，即使是高级用户也可以通过对模型假设进行更高程度的控制而受益，例如与检测疾病爆发和流行病相关的假设。
Background: There is an increasing need for processing and understanding relevant information generated by the systematic collection of public health data over time. However, the analysis of those time series usually requires advanced modeling techniques, which are not necessarily mastered by staff, technicians and researchers working on public health and epidemiology. Here a user-friendly tool, EPIPOI, is presented that facilitates the exploration and extraction of parameters describing trends, seasonality and anomalies that characterize epidemiological processes. It also enables the inspection of those parameters across geographic regions. Although the visual exploration and extraction of relevant parameters from time series data is crucial in epidemiological research, until now it had been largely restricted to specialists.Methods: EPIPOI is freely available software developed in Matlab (The Mathworks Inc) that runs both on PC and Mac computers. Its friendly interface guides users intuitively through useful comparative analyses including the comparison of spatial patterns in temporal parameters.Results: EPIPOI is able to handle complex analyses in an accessible way. A prototype has already been used to assist researchers in a variety of contexts from didactic use in public health workshops to the main analytical tool in published research.Conclusions: EPIPOI can assist public health officials and students to explore time series data using a broad range of sophisticated analytical and visualization tools. It also provides an analytical environment where even advanced users can benefit by enabling a higher degree of control over model assumptions, such as those associated with detecting disease outbreaks and pandemics.