Characterising seasonal influenza epidemiology using primary care surveillance data
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(Published version)
Date
2018
Authors
Cope, R.
Ross, J.
Chilver, M.
Stocks, N.
Mitchell, L.
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Lloyd-Smith, J.
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Journal article
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PLoS Computational Biology, 2018; 14(8):1006377-1-1006377-21
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Robert C. Cope, Joshua V. Ross, Monique Chilver, Nigel P. Stocks, Lewis Mitchell
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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.
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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.