Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/115563
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Type: Journal article
Title: Characterising seasonal influenza epidemiology using primary care surveillance data
Author: Cope, R.
Ross, J.
Chilver, M.
Stocks, N.
Mitchell, L.
Citation: PLoS Computational Biology, 2018; 14(8):1006377-1-1006377-21
Publisher: Public Library of Science (PLoS)
Issue Date: 2018
ISSN: 1553-734X
1553-7358
Editor: Lloyd-Smith, J.
Statement of
Responsibility: 
Robert C. Cope, Joshua V. Ross, Monique Chilver, Nigel P. Stocks, Lewis Mitchell
Abstract: Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons. It can be challenging to obtain highquality data of influenza cases specifically, as clinical presentations with influenza-like symptoms may instead be cases of one of a number of alternate respiratory viruses. We use a new dataset of confirmed influenza virological data from 2011-2016, along with highquality denominators informing a hierarchical observation process, to model seasonal influenza dynamics in New South Wales, Australia. We use approximate Bayesian computation to estimate parameters in a climate-driven stochastic epidemic model, including the basic reproduction number R0, the proportion of the population susceptible to the circulating strain at the beginning of the season, and the probability an infected individual seeks treatment. We conclude that R0 and initial population susceptibility were strongly related, emphasising the challenges of identifying these parameters. Relatively high R0 values alongside low initial population susceptibility were among the results most consistent with these data. Our results reinforce the importance of distinguishing between R0 and the effective reproduction number (Re) in modelling studies.
Keywords: Humans
Population Surveillance
Bayes Theorem
Seasons
Disease Outbreaks
Models, Theoretical
Primary Health Care
Australia
Basic Reproduction Number
Influenza, Human
Adaptive Immunity
Rights: Copyright: © 2018 Cope et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI: 10.1371/journal.pcbi.1006377
Grant ID: http://purl.org/au-research/grants/arc/FT130100254
NHMRC
Published version: http://dx.doi.org/10.1371/journal.pcbi.1006377
Appears in Collections:Aurora harvest 8
Mathematical Sciences publications

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