title: Modelling the seasonal epidemics of respiratory syncytial virus in young children.
abstract: BACKGROUND
  Respiratory 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.
  
  
  OBJECTIVE
  Construct 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.
  
  
  METHODS
  We 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.
  
  
  RESULTS
  From 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.
  
  
  CONCLUSIONS
  Our 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 newborn vaccination program to predict the effect on RSV incidence.
authors: Telethon Kids Institute, University of Western Australia, Perth, Australia.
authors: Hannah C
authors: HC
authors: Moore
authors: Telethon Kids Institute, University of Western Australia, Perth, Australia.
authors: Peter
authors: P
authors: Jacoby
authors: National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
authors: Alexandra B
authors: AB
authors: Hogan
authors: School of Paediatrics and Child Health, University of Western Australia, Perth, Australia; Department of Paediatric and Adolescent Medicine, Princess Margaret Hospital for Children, Perth, Australia.
authors: Christopher C
authors: CC
authors: Blyth
authors: National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
authors: Geoffry N
authors: GN
authors: Mercer
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D004198:Disease Susceptibility; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018357:Respiratory Syncytial Virus Infections / Q000175:diagnosis / Q000453:epidemiology* / Q000635:transmission; D012621:Seasons; D013997:Time Factors; D055815:Young Adult