Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models
<p style="text-align:justify;"> 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 t...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2006
|
| _version_ | 1797101767320666112 |
|---|---|
| author | White, LJ Mandl, JN Gomes, MGM Bodley-Tickell, AT Cane, PA Perez-Brena, P Aguilar, JC Siqueira, MM Portes, SA Straliotto, SM Waris, M Nokes, DJ Medley, GF |
| author_facet | White, LJ Mandl, JN Gomes, MGM Bodley-Tickell, AT Cane, PA Perez-Brena, P Aguilar, JC Siqueira, MM Portes, SA Straliotto, SM Waris, M Nokes, DJ Medley, GF |
| author_sort | White, LJ |
| collection | OXFORD |
| description | <p style="text-align:justify;"> 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. </p> |
| first_indexed | 2024-03-07T05:56:32Z |
| format | Journal article |
| id | oxford-uuid:eab35d24-30b3-4437-b2a7-030caa9f20b4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:56:32Z |
| publishDate | 2006 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:eab35d24-30b3-4437-b2a7-030caa9f20b42022-03-27T11:04:12ZUnderstanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:eab35d24-30b3-4437-b2a7-030caa9f20b4EnglishSymplectic Elements at OxfordElsevier2006White, LJMandl, JNGomes, MGMBodley-Tickell, ATCane, PAPerez-Brena, PAguilar, JCSiqueira, MMPortes, SAStraliotto, SMWaris, MNokes, DJMedley, GF <p style="text-align:justify;"> 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. </p> |
| spellingShingle | White, LJ Mandl, JN Gomes, MGM Bodley-Tickell, AT Cane, PA Perez-Brena, P Aguilar, JC Siqueira, MM Portes, SA Straliotto, SM Waris, M Nokes, DJ Medley, GF Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models |
| title | Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models |
| title_full | Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models |
| title_fullStr | Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models |
| title_full_unstemmed | Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models |
| title_short | Understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models |
| title_sort | understanding the transmission dynamics of respiratory syncytial virus using multiple time series and nested models |
| work_keys_str_mv | AT whitelj understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT mandljn understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT gomesmgm understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT bodleytickellat understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT canepa understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT perezbrenap understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT aguilarjc understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT siqueiramm understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT portessa understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT straliottosm understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT warism understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT nokesdj understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels AT medleygf understandingthetransmissiondynamicsofrespiratorysyncytialvirususingmultipletimeseriesandnestedmodels |