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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hdl_115563.pdf | Published version | 3.79 MB | Adobe PDF | View/Open |
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