Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models
White, L. J., Mandl, J. N., Gomes, M. G. M., Bodley-Tickell, A. T., Cane, P. A., Perez-Brena, P., Aguilar, J. C., Siqueira, M. M., Portes, S. A., Straliotto, S. M., Waris, M., Nokes, D. James and Medley, Graham. (2007) Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models. Mathematical Biosciences, Vol.209 (No.1). pp. 222-239. ISSN 0025-5564Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.mbs.2006.08.018
The nature and role of re-infection and partial immunity are likely to be important determinants of the transmission dynamics of human respiratory syncytial virus (hRSV). We propose a single model structure that captures four possible host responses to infection and subsequent reinfection: partial susceptibility, altered infection duration, reduced infectiousness and temporary immunity (which might be partial). The magnitude of these responses is determined by four homotopy parameters, and by setting some of these parameters to extreme values we generate a set of eight nested, deterministic transmission models. In order to investigate hRSV transmission dynamics, we applied these models to incidence data from eight international locations. Seasonality is included as cyclic variation in transmission. Parameters associated with the natural history of the infection were assumed to be independent of geographic location, while others, such as those associated with seasonality, were assumed location specific. Models incorporating either of the two extreme assumptions for immunity (none or solid and lifelong) were unable to reproduce the observed dynamics. Model fits with either waning or partial immunity to disease or both were visually comparable. The best fitting structure was a lifelong partial immunity to both disease and infection. Observed patterns were reproduced by stochastic simulations using the parameter values estimated from the deterministic models. (C) 2007 Published by Elsevier Inc.
|Item Type:||Journal Article|
|Subjects:||Q Science > QH Natural history > QH301 Biology|
|Divisions:||Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010)|
|Journal or Publication Title:||Mathematical Biosciences|
|Publisher:||Elsevier Science Inc.|
|Number of Pages:||18|
|Page Range:||pp. 222-239|
|Access rights to Published version:||Restricted or Subscription Access|
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