Name
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 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 R-0, 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 R-0 and initial population susceptibility were strongly related, emphasising the challenges of identifying these parameters. Relatively high R-0 values alongside low initial population susceptibility were among the results most consistent with these data. Our results reinforce the importance of distinguishing between R-0 and the effective reproduction number (R-e) in modelling studies. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1371/journal.pcbi.1006377 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 14 | 8 |
ISSN | Citations | PageRank |
1553-7358 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Cope | 1 | 0 | 1.01 |
| Joshua V. Ross | 2 | 2 | 1.79 |
| Monique Chilver | 3 | 0 | 0.34 |
| Nigel P Stocks | 4 | 0 | 0.34 |
| Lewis Mitchell | 5 | 155 | 17.70 |