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Open Access Software

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

Wladimir J Alonso* and Benjamin JJ McCormick

Author Affiliations

Fogarty International Center, National Institutes of Health, Bethesda, 20892, MD, USA

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BMC Public Health 2012, 12:982  doi:10.1186/1471-2458-12-982

Published: 15 November 2012

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.

Keywords:
Epidemiology; Time series; Trends; Seasonality; Anomalies; Data visualization