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Published on: December 10, 2013
Rotavirus within day care centres in Oxfordshire, UK: characterization of partial immunity
L J White1, J Buttery, B Cooper
1Mahidol Oxford Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand. lisa@tropmedres.ac
Insights
Rotavirus reinfection in children attending day care centres (DCCs) is influenced by DCC prevalence. Partial immunity develops over time, reducing disease risk but not infection duration.
Area of Science:
- Epidemiology
- Infectious Disease Modeling
Background:
- Rotavirus is a leading cause of severe diarrheal disease in infants and young children globally.
- Understanding rotavirus transmission dynamics and immunity is crucial for effective control strategies.
Purpose of the Study:
- To model rotavirus transmission and reinfection dynamics in children attending day care centres (DCCs).
- To investigate the impact of previous infection on subsequent rotavirus disease and reinfection risk.
Main Methods:
- Utilized repeated measures data from 14 DCCs in Oxfordshire, UK.
- Developed a biologically realistic probability model for rotavirus incidence and recovery.
- Employed Bayesian Markov chain Monte Carlo methods for parameter estimation.
Main Results:
- Rotavirus transmission in DCCs is dependent on DCC prevalence.
- Symptomatic rotavirus infections are of longer duration than asymptomatic ones.
- Previous infection reduces disease risk and, to a lesser extent, reinfection risk, but not infection duration.
Conclusions:
- Partial immunity to rotavirus infection develops over multiple time scales.
- Day care settings are significant environments for rotavirus transmission and reinfection.
- Findings inform public health interventions for rotavirus prevention and control.
Abstract:
Repeated measures data for rotavirus infection in children within 14 day care centres (DCCs) in the Oxfordshire area, UK, are used to explore aspects of rotavirus transmission and immunity. A biologically realistic model for the transmission of infection is presented as a set of probability models suitable for application to the data. Two transition events are modelled separately: incidence and recovery. The complexity of the underlying mechanistic model is reflected in the choice of the fixed variables in the probability models. Parameter estimation was carried out using a Bayesian Markov chain Monte Carlo method. We use the parameter estimates obtained to build a profile of the natural history of rotavirus reinfection in an individual child. We infer that rotavirus transmission in children in DCCs is dependent on the DCC prevalence, with symptomatic infection of longer duration, but no more infectious per day of infectious period, than asymptomatic infection. There was evidence that a recent previous infection reduces the risk of disease and, to a lesser extent, reinfection, but not duration of infection. The results provide evidence that partial immunity to rotavirus infection develops over several time scales.
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