tetano
Editor, Senior Moderator
PLoS Comput Biol. 2018 Aug 16;14(8):e1006377. doi: 10.1371/journal.pcbi.1006377. [Epub ahead of print]
[h=1]Characterising seasonal influenza epidemiology using primary care surveillance data.[/h] Cope RC[SUP]1[/SUP], Ross JV[SUP]1[/SUP], Chilver M[SUP]2[/SUP], Stocks NP[SUP]2[/SUP], Mitchell L[SUP]1,[/SUP][SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons. It can be challenging to obtain high-quality 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 high-quality 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.
PMID: 30114215 DOI: 10.1371/journal.pcbi.1006377
Free full text
[h=1]Characterising seasonal influenza epidemiology using primary care surveillance data.[/h] Cope RC[SUP]1[/SUP], Ross JV[SUP]1[/SUP], Chilver M[SUP]2[/SUP], Stocks NP[SUP]2[/SUP], Mitchell L[SUP]1,[/SUP][SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons. It can be challenging to obtain high-quality 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 high-quality 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.
PMID: 30114215 DOI: 10.1371/journal.pcbi.1006377
Free full text