Journal article

Retrospective forecasting of the 2010-2014 Melbourne influenza seasons using multiple surveillance systems

R Moss, A Zarebski, P Dawson, JM McCaw



Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, since these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, and we have previously tailored these methods for metropolitan Melbourne (Australia) and Google Flu Trends data. Here we extend these methods to clinical observation and laboratory-confirmation data for Melbourne, on the grounds that these data sources provide more acc..

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Awarded by DSTO project 'Bioterrorism Preparedness Strategic Research Initiative'

Awarded by ARC

Funding Acknowledgements

This work was funded by the DSTO project 'Bioterrorism Preparedness Strategic Research Initiative 07/301'. James M. McCaw is supported by an ARC Future Fellowship (FT110100250). We thank Nicola Stephens, Lucinda Franklin and Trevor Lauer (VDHHS) and James Fielding, Heath Kelly and Kristina Grant (VIDRL) for providing access to, and interpretation of, Victorian influenza surveillance data. We also thank Branko Ristic (DST Group) for his advice and comments concerning particle filtering methods and observation models. We are grateful to the general practitioners who have generously chosen to participate in the VicSPIN sentinel surveillance system.