Imperial College London

Dr Alexandra Hogan

Faculty of MedicineSchool of Public Health

Imperial College Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 3946a.hogan Website

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Moore:2014:10.1371/journal.pone.0100422,
author = {Moore, HC and Jacoby, P and Hogan, AB and Blyth, CC and Mercer, GN},
doi = {10.1371/journal.pone.0100422},
journal = {PLOS One},
pages = {e100422--e100422},
title = {Modelling the seasonal epidemics of respiratory syncytial virus in young children},
url = {http://dx.doi.org/10.1371/journal.pone.0100422},
volume = {9},
year = {2014}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - BackgroundRespiratory syncytial virus (RSV) is a major cause of paediatric morbidity. Mathematical models can be used to characterise annual RSV seasonal epidemics and are a valuable tool to assess the impact of future vaccines.ObjectivesConstruct a mathematical model of seasonal epidemics of RSV and by fitting to a population-level RSV dataset, obtain a better understanding of RSV transmission dynamics.MethodsWe obtained an extensive dataset of weekly RSV testing data in children aged less than 2 years, 2000–2005, for a birth cohort of 245,249 children through linkage of laboratory and birth record datasets. We constructed a seasonally forced compartmental age-structured Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) mathematical model to fit to the seasonal curves of positive RSV detections using the Nelder-Mead method.ResultsFrom 15,830 specimens, 3,394 were positive for RSV. RSV detections exhibited a distinct biennial seasonal pattern with alternating sized peaks in winter months. Our SEIRS model accurately mimicked the observed data with alternating sized peaks using disease parameter values that remained constant across the 6 years of data. Variations in the duration of immunity and recovery periods were explored. The best fit to the data minimising the residual sum of errors was a model using estimates based on previous models in the literature for the infectious period and a slightly lower estimate for the immunity period.ConclusionsOur age-structured model based on routinely collected population laboratory data accurately captures the observed seasonal epidemic curves. The compartmental SEIRS model, based on several assumptions, now provides a validated base model. Ranges for the disease parameters in the model that could replicate the patterns in the data were identified. Areas for future model developments include fitting climatic variables to the seasonal parameter, allowing parameters to vary according to age and implementing a newb
AU - Moore,HC
AU - Jacoby,P
AU - Hogan,AB
AU - Blyth,CC
AU - Mercer,GN
DO - 10.1371/journal.pone.0100422
EP - 100422
PY - 2014///
SN - 1932-6203
SP - 100422
TI - Modelling the seasonal epidemics of respiratory syncytial virus in young children
T2 - PLOS One
UR - http://dx.doi.org/10.1371/journal.pone.0100422
UR - http://hdl.handle.net/10044/1/44609
VL - 9
ER -