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An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection
Published on: December 10, 2013
Predicting age of respiratory syncytial virus infection from birth timing
Chris G McKennan1, Tebeb Gebretsadik2, Steven M Brunwasser3
1Department of Statistics, University of Pittsburgh; Pittsburgh, Pennsylvania, USA. chm195@pitt.edu.
Insights
A new probability model accurately predicts the age of first Respiratory Syncytial Virus (RSV) infection in infants. This tool helps identify children at risk for asthma without costly surveillance.
Area of Science:
- Virology and Epidemiology
- Pediatric Respiratory Health
- Predictive Modeling in Public Health
Background:
- Respiratory Syncytial Virus (RSV) is a common childhood infection, with early-life exposure linked to asthma development.
- Accurate determination of infant RSV infection age is crucial for identifying those at risk of chronic respiratory issues.
- Current surveillance methods are often intensive, costly, and may miss asymptomatic cases.
Purpose of the Study:
- To develop a novel probability model for estimating the age of first RSV infection in infants.
- To provide a reliable method for identifying infants at risk for respiratory sequelae without extensive surveillance.
Main Methods:
- Developed a probability model incorporating infant birthdates, demographic factors, and public RSV circulation data.
- The model calculates the probability of first RSV infection at any age from birth to one year.
- Validated the model across four independent datasets from the United States, including two independent cohorts.
Main Results:
- The model accurately predicts the age of first RSV infection in infants.
- It accounts for approximately 37% of the variance in the age of first infection.
- The model demonstrates generalizability across diverse datasets and cohorts.
Conclusions:
- This probability model offers a reliable and cost-effective approach to estimate infant RSV infection timing.
- Facilitates the identification of infants susceptible to chronic respiratory conditions linked to early RSV exposure.
- Eliminates the need for intensive active surveillance to determine the age of first RSV infection.
Abstract:
Respiratory syncytial virus (RSV) infects nearly all children by age 2 to 3 years, and early-life infection-defined using active and passive surveillance with quantitative polymerase chain reaction- and serology-identified infection-has been implicated as a causal factor in childhood asthma. As such, identifying infants that are likely to be infected with RSV during this critical susceptibility window has important implications for identifying individuals at risk for chronic respiratory sequelae. However, determining the age of RSV infection in large populations is challenging because many infections are asymptomatic, making accurate detection dependent on intensive and costly surveillance. To address this, we developed a probability model for age of first RSV infection. It uses an infant's birthdate, demographic covariates, and publicly available RSV circulation data to determine the probability they were first infected at any age from birth to one year. Our model is interpretable, accounts for nearly 37% of the variance in age at first infection, and generalizes across four independent datasets collected from participants in the United States, where we use it to accurately predict age of first infection in two independent cohorts. Our work facilitates reliable estimation of the age of infant RSV infection during the first year of life without the need for active surveillance.
