Skip to main content
Top
Published in: BMC Public Health 1/2012

Open Access 01-12-2012 | Software

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

Authors: Wladimir J Alonso, Benjamin JJ McCormick

Published in: BMC Public Health | Issue 1/2012

Login to get access

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.
Appendix
Available only for authorised users
Literature
1.
go back to reference Lee LM, Thacker SB: Public health surveillance and knowing about health in the context of growing sources of health data. Am J Prev Med. 2011, 41: 636-640. 10.1016/j.amepre.2011.08.015.CrossRefPubMed Lee LM, Thacker SB: Public health surveillance and knowing about health in the context of growing sources of health data. Am J Prev Med. 2011, 41: 636-640. 10.1016/j.amepre.2011.08.015.CrossRefPubMed
2.
go back to reference Simonsen L, Clarke MJ, Williamson GD, Stroup DF, Arden NH, Schonberger LB: The impact of influenza epidemics on mortality: introducing a severity index. Am J Public Health. 1997, 87: 1944-1950. 10.2105/AJPH.87.12.1944.CrossRefPubMedPubMedCentral Simonsen L, Clarke MJ, Williamson GD, Stroup DF, Arden NH, Schonberger LB: The impact of influenza epidemics on mortality: introducing a severity index. Am J Public Health. 1997, 87: 1944-1950. 10.2105/AJPH.87.12.1944.CrossRefPubMedPubMedCentral
3.
go back to reference Viboud C, Boëlle P-Y, Pakdaman K, Carrat F, Valleron A-J, Flahault A: Influenza epidemics in the United States, France, and Australia, 1972–1997. Emerging Infect Dis. 2004, 10: 32-39. 10.3201/eid1001.020705.CrossRefPubMedPubMedCentral Viboud C, Boëlle P-Y, Pakdaman K, Carrat F, Valleron A-J, Flahault A: Influenza epidemics in the United States, France, and Australia, 1972–1997. Emerging Infect Dis. 2004, 10: 32-39. 10.3201/eid1001.020705.CrossRefPubMedPubMedCentral
4.
go back to reference Serfling RE: Methods for current statistical analysis of excess pneumonia-influenza deaths. Pub Health Rep. 1963, 78: 494-506. 10.2307/4591848.CrossRef Serfling RE: Methods for current statistical analysis of excess pneumonia-influenza deaths. Pub Health Rep. 1963, 78: 494-506. 10.2307/4591848.CrossRef
5.
go back to reference Schuck-Paim C, Viboud C, Simmonsen L, Miller MA, Moura FEA, Fernandes RM, Carvalho ML, Alonso WJ: Were equatorial regions less affected by the 2009 influenza pandemic? The Brazilian experience. PLoS ONE. 2012, 7: e41918-10.1371/journal.pone.0041918.CrossRefPubMedPubMedCentral Schuck-Paim C, Viboud C, Simmonsen L, Miller MA, Moura FEA, Fernandes RM, Carvalho ML, Alonso WJ: Were equatorial regions less affected by the 2009 influenza pandemic? The Brazilian experience. PLoS ONE. 2012, 7: e41918-10.1371/journal.pone.0041918.CrossRefPubMedPubMedCentral
6.
go back to reference Viboud C, Bjørnstad ON, Smith DL, Simonsen L, Miller MA, Grenfell BT: Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza. Science. 2006, 312: 447-451. 10.1126/science.1125237.CrossRefPubMed Viboud C, Bjørnstad ON, Smith DL, Simonsen L, Miller MA, Grenfell BT: Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza. Science. 2006, 312: 447-451. 10.1126/science.1125237.CrossRefPubMed
7.
go back to reference Alonso WJ, Viboud C, Simonsen L, Hirano EW, Daufenbach LZ, Miller MA: Seasonality of influenza in Brazil: a traveling wave from the Amazon to the subtropics. Am J Epidemiol. 2007, 165: 1434-1442. 10.1093/aje/kwm012.CrossRefPubMed Alonso WJ, Viboud C, Simonsen L, Hirano EW, Daufenbach LZ, Miller MA: Seasonality of influenza in Brazil: a traveling wave from the Amazon to the subtropics. Am J Epidemiol. 2007, 165: 1434-1442. 10.1093/aje/kwm012.CrossRefPubMed
8.
go back to reference Cummings DAT, Irizarry RA, Huang NE, Endy TP, Nisalak A, Ungchusak K, Burke DS: Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature. 2004, 427: 344-347. 10.1038/nature02225.CrossRefPubMed Cummings DAT, Irizarry RA, Huang NE, Endy TP, Nisalak A, Ungchusak K, Burke DS: Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature. 2004, 427: 344-347. 10.1038/nature02225.CrossRefPubMed
9.
go back to reference Grenfell BT, Bjørnstad ON, Kappey J: Travelling waves and spatial hierarchies in measles epidemics. Nature. 2001, 414: 716-723. 10.1038/414716a.CrossRefPubMed Grenfell BT, Bjørnstad ON, Kappey J: Travelling waves and spatial hierarchies in measles epidemics. Nature. 2001, 414: 716-723. 10.1038/414716a.CrossRefPubMed
10.
go back to reference McCormick BJJ, Alonso WJ, Miller MA: An exploration of spatial patterns of seasonal diarrhoeal morbidity in Thailand. Epidemiol Infect. 2012, 140: 1236-1243. 10.1017/S0950268811001919.CrossRefPubMed McCormick BJJ, Alonso WJ, Miller MA: An exploration of spatial patterns of seasonal diarrhoeal morbidity in Thailand. Epidemiol Infect. 2012, 140: 1236-1243. 10.1017/S0950268811001919.CrossRefPubMed
11.
go back to reference Alonso WJ, Acuña-Soto R, Giglio R, Nuckols J, Leyk S, Schuck-Paim C, Viboud C, Miller MA, McCormick BJJ: Spatio-temporal patterns of diarrhoeal mortality in Mexico. Epidemiol Infect. 2012, 140: 91-99. 10.1017/S0950268811000562.CrossRefPubMed Alonso WJ, Acuña-Soto R, Giglio R, Nuckols J, Leyk S, Schuck-Paim C, Viboud C, Miller MA, McCormick BJJ: Spatio-temporal patterns of diarrhoeal mortality in Mexico. Epidemiol Infect. 2012, 140: 91-99. 10.1017/S0950268811000562.CrossRefPubMed
14.
go back to reference Hammer O: Palaeontological Statistics (PAST). 2010, Oslo Hammer O: Palaeontological Statistics (PAST). 2010, Oslo
15.
go back to reference Legendre P, Legendre L: Numerical ecology. 1998, Amsterdam, The Netherlands: Elsevier Science, 20- Legendre P, Legendre L: Numerical ecology. 1998, Amsterdam, The Netherlands: Elsevier Science, 20-
16.
go back to reference Cleveland RB, Cleveland WS, McRae JE, Terpenning I: STL: A seasonal-trend decomposition procedure based on loess. J Official Stat. 1990, 6: 3-73. Cleveland RB, Cleveland WS, McRae JE, Terpenning I: STL: A seasonal-trend decomposition procedure based on loess. J Official Stat. 1990, 6: 3-73.
17.
18.
go back to reference Rogers DJ, Hay SI, Packer MJ: Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Annals Trop Med Parasitol. 1996, 90: 225-242. Rogers DJ, Hay SI, Packer MJ: Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Annals Trop Med Parasitol. 1996, 90: 225-242.
19.
20.
go back to reference Pelat C, Boelle PY, Cowling BJ, Carrat F, Flahault A, Ansart S, Valleron AJ: Online detection and quantification of epidemics. BMC Med Info Decision Making. 2007, 7: 29-10.1186/1472-6947-7-29.CrossRef Pelat C, Boelle PY, Cowling BJ, Carrat F, Flahault A, Ansart S, Valleron AJ: Online detection and quantification of epidemics. BMC Med Info Decision Making. 2007, 7: 29-10.1186/1472-6947-7-29.CrossRef
21.
go back to reference Housworth J, Langmuir AD: Excess mortality from epidemic influenza, 1957–1966. Am J Epidemiol. 1974, 100: 40-48.PubMed Housworth J, Langmuir AD: Excess mortality from epidemic influenza, 1957–1966. Am J Epidemiol. 1974, 100: 40-48.PubMed
22.
23.
go back to reference Purse BV, McCormick BJJ, Mellor PS, Baylis M, Boorman J, Borras D, Burgu I, Capela R, Caracappa S, Collantes F: Incriminating bluetongue virus vectors with climate envelope models. J Appl Ecol. 2007, 44: 1231-1242. 10.1111/j.1365-2664.2007.01342.x.CrossRef Purse BV, McCormick BJJ, Mellor PS, Baylis M, Boorman J, Borras D, Burgu I, Capela R, Caracappa S, Collantes F: Incriminating bluetongue virus vectors with climate envelope models. J Appl Ecol. 2007, 44: 1231-1242. 10.1111/j.1365-2664.2007.01342.x.CrossRef
24.
go back to reference Olsson L, Eklund L: Fourier Series for analysis of temporal sequences of satellite sensor imagery. Int J Remote Sensing. 1994, 15: 3735-3741. 10.1080/01431169408954355.CrossRef Olsson L, Eklund L: Fourier Series for analysis of temporal sequences of satellite sensor imagery. Int J Remote Sensing. 1994, 15: 3735-3741. 10.1080/01431169408954355.CrossRef
25.
go back to reference Ansart S, Pelat C, Boelle P-Y, Carrat F, Flahault A, Valleron A-J: Mortality burden of the 1918–1919 influenza pandemic in Europe. Influenza Other Respi Viruses. 2009, 3: 99-106. 10.1111/j.1750-2659.2009.00080.x.CrossRef Ansart S, Pelat C, Boelle P-Y, Carrat F, Flahault A, Valleron A-J: Mortality burden of the 1918–1919 influenza pandemic in Europe. Influenza Other Respi Viruses. 2009, 3: 99-106. 10.1111/j.1750-2659.2009.00080.x.CrossRef
26.
go back to reference Simonsen L, Reichert TA, Viboud C, Blackwelder WC, Taylor RJ, Miller MA: Impact of influenza vaccination on seasonal mortality in the US elderly population. Arch Intern Med. 2005, 165: 265-272.CrossRefPubMed Simonsen L, Reichert TA, Viboud C, Blackwelder WC, Taylor RJ, Miller MA: Impact of influenza vaccination on seasonal mortality in the US elderly population. Arch Intern Med. 2005, 165: 265-272.CrossRefPubMed
27.
go back to reference Torrence C, Compo GP: A practical guide to wavelet analysis. Bull Am Met Soc. 1998, 79: 61-78. 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.CrossRef Torrence C, Compo GP: A practical guide to wavelet analysis. Bull Am Met Soc. 1998, 79: 61-78. 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.CrossRef
28.
go back to reference Johansson MA, Cummings DAT, Glass GE: Multiyear Climate Variability and Dengue—El Niño Southern Oscillation, Weather, and Dengue Incidence in Puerto Rico, Mexico, and Thailand: A Longitudinal Data Analysis. PLoS Med. 2009, 6: e1000168-10.1371/journal.pmed.1000168.CrossRefPubMedPubMedCentral Johansson MA, Cummings DAT, Glass GE: Multiyear Climate Variability and Dengue—El Niño Southern Oscillation, Weather, and Dengue Incidence in Puerto Rico, Mexico, and Thailand: A Longitudinal Data Analysis. PLoS Med. 2009, 6: e1000168-10.1371/journal.pmed.1000168.CrossRefPubMedPubMedCentral
29.
go back to reference Diggle PJ, Zeger SL: Editorial. Biostat. 2010, 11: 375-375. 10.1093/biostatistics/kxq029.CrossRef Diggle PJ, Zeger SL: Editorial. Biostat. 2010, 11: 375-375. 10.1093/biostatistics/kxq029.CrossRef
30.
go back to reference Erbas , Hyndman R: Data visualisation for time series in environmental epidemiology. J Epidemiol Biostat. 2001, 6: 433-443. 10.1080/135952201317225462.CrossRefPubMed Erbas , Hyndman R: Data visualisation for time series in environmental epidemiology. J Epidemiol Biostat. 2001, 6: 433-443. 10.1080/135952201317225462.CrossRefPubMed
31.
go back to reference Taleb N: The black swan : the impact of the highly improbable. 2007, New York: Random House Taleb N: The black swan : the impact of the highly improbable. 2007, New York: Random House
Metadata
Title
EPIPOI: A user-friendly analytical tool for the extraction and visualization of temporal parameters from epidemiological time series
Authors
Wladimir J Alonso
Benjamin JJ McCormick
Publication date
01-12-2012
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2012
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/1471-2458-12-982

Other articles of this Issue 1/2012

BMC Public Health 1/2012 Go to the issue