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Modelling forces of infection for measles, mumps and rubella
1Communicable Disease Surveillance Centre, London, U.K.
Abstract:
Serological data from 8870 persons collected prior to the introduction of measles, mumps and rubella (MMR) vaccine in the UK are used to describe the rate at which individuals acquire infection by these diseases at different ages. A parsimonious model is developed and fitted under various interpretations of the data, particularly concerning the probability of lifelong susceptibility to infection. It is shown that, while the force of infection curves are relatively robust in their general features, they exhibit considerable sensitivity in matters of important detail. This is true in particular of the values taken by the force of infection in older age groups. As a result, estimates of the average age at infection are highly sensitive to these interpretations. This in turn may limit the accuracy of predictions from mathematical models based on these parameters, in particular regarding the level of immunization required for eradication of disease.
Insights
Pre-vaccine serological data reveal that measles, mumps, and rubella (MMR) infection rates vary by age. Model interpretations significantly impact infection force estimates, affecting disease eradication predictions.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Immunology
Background:
- Measles, mumps, and rubella (MMR) are significant public health concerns.
- Understanding age-specific infection dynamics is crucial for disease control.
Purpose of the Study:
- To analyze pre-vaccine serological data to determine age-specific infection rates for measles, mumps, and rubella.
- To develop and apply a mathematical model to assess the impact of different data interpretations on infection dynamics.
Main Methods:
- Utilized serological data from 8870 individuals collected before MMR vaccine introduction in the UK.
- Developed and fitted a parsimonious mathematical model to the data.
- Explored various interpretations of lifelong susceptibility to infection.
Main Results:
- Force of infection curves showed general robustness but significant sensitivity in detail, especially for older age groups.
- Estimates of the average age at infection were highly sensitive to model interpretations.
- The accuracy of disease eradication predictions may be limited by parameter sensitivity.
Conclusions:
- Interpreting pre-vaccine MMR infection data requires careful consideration of model assumptions.
- Sensitivity in infection force estimates, particularly for older age groups, impacts the accuracy of disease eradication modeling.
- Further research is needed to refine models for precise immunization strategy development.