A world-first RSV transmission model developed by The Kids Research Institute Australia has used real data to mimic RSV patterns seen in young children as a tool to predict the impact of Western Australia’s RSV immunisation program.
Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory infections in children worldwide, with babies under six months accounting for 39 per cent of hospitalisations.
Newly available therapeutics, like the long-acting antibody treatment Nirsevimab, have prompted calls for mathematical models to identify the highest-risk groups who would benefit most from immunisation.
The study, published in the journal BMC Infectious Diseases, developed a model that was fitted to more than 200,000 individually linked health records between 2000 and 2012 to mimic the patterns of RSV-hospitalisation rates in high-risk groups.
With young age and pre-term birth status strong predictors of RSV-related hospitalisation, the model uses an age-to-risk function to capture any changes to risk over time – a feature of the model that is a first-of its-kind.
The age-to-risk function revealed the hospitalisation risk given infection dropped rapidly over time for both pre-term and full-term babies – yet the risk for pre-term babies was still higher.
Senior author Associate Professor Hannah Moore, from the Wesfarmers Centre of Vaccines and Infectious Diseases at The Kids Research Institute Australia and Curtin University’s School of Population Health, said mathematical modelling was a key tool to understand RSV transmission, examine the impact on health care systems and guide interventions.
“We developed a model based on age to identify critical targets who would benefit most from RSV immunisation and future vaccination,” Associate Professor Moore said.
"The model’s age-to-risk function shows babies’ hospitalisation risk following an RSV infection peaks at zero to three months and then declines rapidly over 12 months for all babies; yet a higher risk still exists for pre-term babies.
“We also discovered the risk of hospitalisation dropped to less than 10 per cent by seven months of age for full-term babies and by nine months for pre-term babies.”
Associate Professor Moore – who recently led a public RSV webinar to raise awareness about immunisation – said these findings helped to pinpoint the best targets and ages to immunise.
“Findings show the best time to immunise is in the very early days of life, which has guided the State Government to recommend Nirsevimab for newborns before they even leave the maternity wards.”
Roll-out of Nirsevimab in maternity wards started in April this year, with the move expected to cut the chances of hospitalisation for an acute lower respiratory infection by around 80 per cent.
Lead author and mathematician Fiona Giannini, from the Wesfarmers Centre of Vaccines and Infectious Diseases, said the model could be extended to examine other risk factors while also predicting the impact of the broader RSV immunisation program.
“The model’s adaptability is useful to provide data-driven strategic decisions to create the most effective RSV immunisation programs, in terms of health and cost, to relieve the burden of this disease,” Ms Giannini said.
"A program without data driving key decisions, would result in improper public spend and poorer health outcomes – epidemiology modelling ensures we get the most from public health programs."
The article Modelling RSV age-specific risk of hospitalisation in term and pre-term infants is available here.