Modelling the seasonal epidemics of respiratory syncytial virus in young children

Hannah C Moore; Peter Jacoby; Alexandra B Hogan; Christopher C Blyth; Geoffry N Mercer

doi:10.1371/journal.pone.0100422

Modelling the seasonal epidemics of respiratory syncytial virus in young children

PLoS One. 2014 Jun 26;9(6):e100422. doi: 10.1371/journal.pone.0100422. eCollection 2014.

Authors

Hannah C Moore¹, Peter Jacoby¹, Alexandra B Hogan², Christopher C Blyth³, Geoffry N Mercer²

Affiliations

¹ Telethon Kids Institute, University of Western Australia, Perth, Australia.
² National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
³ 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.

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.

Objectives: 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Age Factors
Aged
Aged, 80 and over
Child
Child, Preschool
Disease Susceptibility
Female
Humans
Infant
Infant, Newborn
Male
Middle Aged
Models, Statistical*
Respiratory Syncytial Virus Infections / diagnosis
Respiratory Syncytial Virus Infections / epidemiology*
Respiratory Syncytial Virus Infections / transmission
Seasons*
Time Factors
Young Adult

Grants and funding

HCM is funded by National Health and Medical Research Council Fellowship #1034254. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.