EPIPOI: A user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series

Wladimir J. Alonso*, Benjamin J.J. McCormick

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number982
JournalBMC Public Health
Volume12
Issue number1
DOIs
Publication statusPublished - 2012

Keywords

  • Anomalies
  • Data visualization
  • Epidemiology
  • Seasonality
  • Time series
  • Trends

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