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

Part of: Numerical methods in Fourier analysis Statistics

Published online by Cambridge University Press:  21 April 2017

A. B. HOGAN Open the ORCID record for A. B. HOGAN [Opens in a new window] ,
K. GLASS  and
R. S. ANDERSSEN
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
*
✉alexandra.hogan@anu.edu.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.

Keywords

seasonalityperiodicityinfectious diseaseepidemicinfluenzatime series analysis

MSC classification

Primary: 65T99: None of the above, but in this section
Secondary: 62-07: Data analysis
Type
Research Article
Information
The ANZIAM Journal , Volume 59 , Issue 1 , July 2017 , pp. 51 - 60
Copyright
© 2017 Australian Mathematical Society 

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