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Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data

  • L. J. Martin (a1), H. Dong (a1), Q. Liu (a1), J. Talbot (a1), W. Qiu (a1) and Y. Yasui (a1) (a2)...

Abstract

Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004–31 July 2008) and tested (testing data, 1 August 2008–19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ±30% of observed volumes for 67%–82% of high-volume days and within 0.3%–21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

Author for correspondence: L. J. Martin, E-mail: leahjmartin1@gmail.com; leah.martin@ualberta.ca

Footnotes

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Previous presentations: Our modelling was based on preliminary analyses, which were presented at the University of Alberta School of Public Health Insights meeting (2010); Canadian Public Health Association Conference (2012); Society for Epidemiological Research (2012) and formed part of Ms. W Qiu's MSc thesis (University of Alberta, 2012). We also presented a more updated analysis at the International Meeting on Emerging Infectious Diseases (2014).

Footnotes

References

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1.Menec, VH et al. (2001) The Impact of Influenza-Like Illness on the Winnipeg Health Care System: Is an Early Warning System Possible? Faculty of Medicine, University of Manitoba. Available at http://mchp-appserv.cpe.umanitoba.ca/reference/flu-like.pdf (Accessed 4 October 2012).
2.Menec, VH et al. (2003) The impact of influenza-associated respiratory illnesses on hospitalizations, physician visits, emergency room visits, and mortality. Canadian Journal of Public Health 94, 5963.
3.Martin, LJ et al. (2017) Influenza-like illness-related emergency department visits: Christmas and New Year holiday peaks and relationships with laboratory-confirmed respiratory virus detections, Edmonton, Alberta, 2004–2014. Influenza and Other Respiratory Viruses 11, 3340.
4.Gerein, K (2014) Edmonton runs out of vaccine, as specialized clinic for flu patients opens. Edmonton Journal, January 11, 2014.
5.Chu, A et al. (2012) The use of syndromic surveillance for decision-making during the H1N1 pandemic: a qualitative study. BMC Public Health 12, 929.
6.Henning, KJ (2004) What is syndromic surveillance? MMWR Morbidity and Mortality Weekly Report 53(Suppl), 511.
7.Fan, S et al. (2010) A multi-function public health surveillance system and the lessons learned in its development: the Alberta Real Time Syndromic Surveillance Net. Canadian Journal of Public Health 101, 454458.
8.Anon. Health Link Alberta. Available at http://www.albertahealthservices.ca/223.asp (Accessed 26 March 2015).
9.Harrell, F (2001) Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis. Chapter 5.3. New York: Springer.
10.Alberta Health Services. Pandemic (H1N1) 2009: The Alberta Experience; 15 December 2010. Available at http://www.health.alberta.ca/documents/H1N1-Alberta-Experience-2010.pdf (Accessed 1 March 2012).
11.Statistics Canada. Cumulative profile, 2006 – Alberta (147 areas) (table), 2006 Census of Population, 3 char. postal code (Forward Sortation Areas) (database), Using E-STAT (distributor). Available at http://estat.statcan.gc.ca/cgi-win/cnsmcgi.exe?Lang=E&EST-Fi=EStat\English\SC_RR-eng.htm (Accessed 16 January 2012).
12.Akaike, H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716723.
13.Bishop, CM (2006) Pattern Recognition and Machine Learning. New York, NY: Springer, p. 738.
14.R Core Team (2013) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
15.Viboud, C et al. (2003) Prediction of the spread of influenza epidemics by the method of analogues. American Journal of Epidemiology 158, 9961006.
16.Kleinman, K, Lazarus, R and Platt, R (2004) A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. American Journal of Epidemiology 159, 217224.
17.Perry, AG et al. (2010) A comparison of methods for forecasting emergency department visits for respiratory illness using telehealth Ontario calls. Canadian Journal of Public Health 101, 464469.
18.Chan, TC et al. (2010) Probabilistic daily ILI syndromic surveillance with a spatio-temporal Bayesian hierarchical model. PLoS One 5, e11626.
19.Dugas, AF et al. (2013) Influenza forecasting with Google Flu Trends. PLoS One 8, e56176.
20.Cook, S et al. (2011) Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One 6, e23610.
21.The Flu Trends Team. The Next Chapter for Flu Trends. Google Research Blog. Available at http://googleresearch.blogspot.ca/2015/08/the-next-chapter-for-flutrends.html. Published 20 August 2015 (Accessed 29 September 2015).

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Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data

  • L. J. Martin (a1), H. Dong (a1), Q. Liu (a1), J. Talbot (a1), W. Qiu (a1) and Y. Yasui (a1) (a2)...

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