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

R. Moss (a1), A. Zarebski (a2), P. Dawson (a3) and J. M. McCaw (a1) (a2) (a4).Retrospective forecasting of the 2010–2014 Melbourne influenza seasons using multiple surveillance systemsEpidemiology and Infection, 145(1), 156-169. doi:10.1017/S0950268816002053. Published online: 27 September 2016


Abstract

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 accurate characterizations of influenza activity. We show that from each of these data sources we can accurately predict the timing of the epidemic peak 4–6 weeks in advance. We also show that making simultaneous use of multiple surveillance systems to improve forecast skill remains a fundamental challenge. Disparate systems provide complementary characterizations of disease activity, which may or may not be comparable, and it is unclear how a ‘ground truth’ for evaluating forecasts against these multiple characterizations might be defined. These findings are a significant step towards making optimal use of routine surveillance data for outbreak forecasting.

*Author for correspondence: Dr R. Moss, Level 3, 207 Bouverie Street, The University of Melbourne, Victoria 3010, Australia. (Email: rgmoss@unimelb.edu.au)

COPYRIGHT: © Cambridge University Press 2016

(a1)Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia

(a2)School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia

(a3)Land Personnel Protection Branch, Land Division, Defence Science and Technology Group, Melbourne, Australia

(a4)Modelling & Simulation, Murdoch Children’s Research Institute, Royal Children’s Hospital, Melbourne, Australia

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