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COMPLEX DEMODULATION: A NOVEL TIME SERIES METHOD FOR ANALYSING SEASONAL INFECTIOUS DISEASES

Published online by Cambridge University Press:  21 April 2017

A. B. HOGAN*
Affiliation:
Research School of Population Health, The Australian National University, Canberra, Australia email alexandra.hogan@anu.edu.au, kathryn.glass@anu.edu.au
K. GLASS
Affiliation:
Research School of Population Health, The Australian National University, Canberra, Australia email alexandra.hogan@anu.edu.au, kathryn.glass@anu.edu.au
R. S. ANDERSSEN
Affiliation:
CSIRO Data61, Canberra, Australia email bob.anderssen@data61.csiro.au
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Abstract

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Understanding how seasonal patterns change from year to year is important for the management of infectious disease epidemics. Here, we present a mathematical formalization of the application of complex demodulation, which has previously only been applied in an exploratory manner in the context of infectious diseases. This method extracts the changing amplitude and phase from seasonal data, allowing comparisons between the size and timing of yearly epidemics. We first validate the method using synthetic data that displays the key features of epidemic data. In particular, we analyse both annual and biennial synthetic data, and explore the effect of delayed epidemics on the extracted amplitude and phase. We then demonstrate the usefulness of complex demodulation using national notification data for influenza in Australia. This method clearly highlights the higher number of notifications and the early peak of the influenza pandemic in 2009. We also identify that epidemics that peaked later than usual generally followed larger epidemics and involved fewer overall notifications. Our analysis establishes a role for complex demodulation in the study of seasonal epidemiological events.

Type
Research Article
Copyright
© 2017 Australian Mathematical Society 

References

Australian Government Department of Health, “Number of notifications of Influenza (laboratory confirmed), Australia, 2016”, http://www9.health.gov.au/cda/source/cda-index.cfm.Google Scholar
Bloomfield, P., Fourier analysis of time series: An introduction (Wiley, New York, 2000).CrossRefGoogle Scholar
Cazelles, B., Cazelles, K. and Chavez, M., “Wavelet analysis in ecology and epidemiology: impact of statistical tests”, J. R. Soc. Interface 11 (2014) 110; doi:10.1098/rsif.2013.0585.Google ScholarPubMed
Finkenstädt, B. and Grenfell, B., “Empirical determinants of measles metapopulation dynamics in England and Wales”, Proc. R. Soc. Lond. B 265 (1998) 211220; doi:10.1098/rspb.1998.0284.CrossRefGoogle ScholarPubMed
Fisman, D. N., “Seasonality of infectious diseases”, Ann. Rev. Pub. Health 28 (2007) 127143; doi:10.1146/annurev.publhealth.28.021406.14412.CrossRefGoogle ScholarPubMed
Grassly, N. C. and Fraser, C., “Seasonal infectious disease epidemiology”, Proc. R. Soc. B 273 (2006) 25412550; doi:10.1098/rspb.2006.3604.CrossRefGoogle ScholarPubMed
Grenfell, B. T., Bjørnstad, O. N. and Kappey, J., “Travelling waves and spatial hierarchies in measles epidemics”, Nature 414 (2001) 716723; doi:10.1038/414716a.CrossRefGoogle ScholarPubMed
Hao, Y.-L., Ueda, Y. and Ishii, N., “Improved procedure of complex demodulation and an application to frequency analysis of sleep spindles in EEG”, Med. Biol. Eng. Comput. 30 (1992) 406412; doi:10.1007/BF02446168.CrossRefGoogle ScholarPubMed
Hayano, J., Taylor, J. A., Yamada, A., Mukai, S., Hori, R., Asakawa, T., Yokoyama, K., Watanabe, Y., Takata, K. and Fujinami, T., “Continuous assessment of hemodynamic control by complex demodulation of cardiovascular variability”, Am. J. Physiol. 264 (1993) H1229–H1238; http://ajpheart.physiology.org/content/264/4/H1229.long.Google ScholarPubMed
Hogan, A. B., Anderssen, R. S., Davis, S., Moore, H. C., Lim, F. J., Fathima, P. and Glass, K., “Time series analysis of RSV and bronchiolitis seasonality in temperate and tropical Western Australia”, Epidemics 16 (2016) 49–55; doi.10.1016/j.epidem.2016.05.001.CrossRefGoogle ScholarPubMed
Kingan, P. A., Bloomfield, P. and Anderssen, R. S., “Phase drift and coherence in geomagnetic data during a magnetic storm (Dst)”, J. Geomagn. Geoelectr. 32 (1980) 5765; doi:10.5636/jgg.32.57.CrossRefGoogle Scholar
Kondo, H., Ozone, M., Ohki, N., Sagawa, Y., Yamamichi, K., Fukuju, M., Yoshida, T., Nishi, C., Kawasaki, A., Mori, K., Kanbayashi, T., Izumi, M., Hishikawa, Y., Nishino, S. and Shimizu, T., “Association between heart rate variability, blood pressure and autonomic activity in cyclic alternating pattern during sleep”, Sleep 37 (2014) 187194; doi:10.5665/sleep.3334.CrossRefGoogle ScholarPubMed
Lofgren, E., Fefferman, N. H., Naumov, Y. N., Gorski, J. and Naumova, E. N., “Influenza seasonality: underlying causes and modeling theories”, J. Virol. 81 (2007); doi:10.1128/JVI.01680-06.CrossRefGoogle ScholarPubMed
Nader, I. W., Pietschnig, J., Niederkrotenthaler, T., Kapusta, N. D., Sonneck, G. and Voracek, M., “Suicide seasonality: complex demodulation as a novel approach in epidemiologic analysis”, PLoS ONE 6 (2011) e17413; doi:10.1371/journal.pone.0017413.CrossRefGoogle ScholarPubMed
Pitzer, V. E., Viboud, C., Alonso, W. J., Wilcox, T., Metcalf, C. J., Steiner, C. A., Haynes, A. K. and Grenfell, B. T., “Environmental drivers of the spatiotemporal dynamics of respiratory syncytial virus in the United States”, PLoS Pathog. 11 (2015) e1004591; doi:10.1371/journal.ppat.1004591.CrossRefGoogle ScholarPubMed
Priestley, M. B., “Wavelets and time-dependent spectral analysis”, J. Time Series Anal. 17 (1996) 85103; doi:10.1111/j.1467-9892.1996.tb00266.x.CrossRefGoogle Scholar
Sasai, T., Matsuura, M. and Inoue, Y., “Change in heart rate variability precedes the occurrence of periodic leg movements during sleep: an observational study”, BMC Neurol. 13 (2013);doi:10.1186/1471-2377-13-139.CrossRefGoogle Scholar
Thomas, J. B., An introduction to statistical communication theory (Wiley, New York, 1969).Google Scholar
Viboud, C., Alonso, W. J. and Simonsen, L., “Influenza in tropical regions”, PLoS Med. 3 (2006) 468471; doi:10.1371/journal.pmed.0030089.CrossRefGoogle ScholarPubMed
Yasuma, F. and Hayano, J., “Respiratory sinus arrhythmia: why does the heartbeat synchronize with respiratory rhythm?”, Chest 125 (2004) 683690; doi:10.1378/chest.125.2.683.CrossRefGoogle ScholarPubMed
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COMPLEX DEMODULATION: A NOVEL TIME SERIES METHOD FOR ANALYSING SEASONAL INFECTIOUS DISEASES
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