Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/115563
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cope, R. | - |
dc.contributor.author | Ross, J. | - |
dc.contributor.author | Chilver, M. | - |
dc.contributor.author | Stocks, N. | - |
dc.contributor.author | Mitchell, L. | - |
dc.contributor.editor | Lloyd-Smith, J. | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | PLoS Computational Biology, 2018; 14(8):1006377-1-1006377-21 | - |
dc.identifier.issn | 1553-734X | - |
dc.identifier.issn | 1553-7358 | - |
dc.identifier.uri | http://hdl.handle.net/2440/115563 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Robert C. Cope, Joshua V. Ross, Monique Chilver, Nigel P. Stocks, Lewis Mitchell | - |
dc.language.iso | en | - |
dc.publisher | Public Library of Science (PLoS) | - |
dc.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. | - |
dc.source.uri | http://dx.doi.org/10.1371/journal.pcbi.1006377 | - |
dc.subject | Humans | - |
dc.subject | Population Surveillance | - |
dc.subject | Bayes Theorem | - |
dc.subject | Seasons | - |
dc.subject | Disease Outbreaks | - |
dc.subject | Models, Theoretical | - |
dc.subject | Primary Health Care | - |
dc.subject | Australia | - |
dc.subject | Basic Reproduction Number | - |
dc.subject | Influenza, Human | - |
dc.subject | Adaptive Immunity | - |
dc.title | Characterising seasonal influenza epidemiology using primary care surveillance data | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1371/journal.pcbi.1006377 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FT130100254 | - |
dc.relation.grant | NHMRC | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Cope, R. [0000-0002-2048-858X] | - |
dc.identifier.orcid | Ross, J. [0000-0002-9918-8167] | - |
dc.identifier.orcid | Chilver, M. [0000-0001-6369-8483] | - |
dc.identifier.orcid | Stocks, N. [0000-0002-9018-0361] | - |
dc.identifier.orcid | Mitchell, L. [0000-0001-8191-1997] | - |
Appears in Collections: | Aurora harvest 8 Mathematical Sciences publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
hdl_115563.pdf | Published version | 3.79 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.