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Age-structure and transient dynamics in epidemiological systems
F M G Magpantay1, A A King2, P Rohani3
1Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada K7L 3N6.
Journal of the Royal Society, Interface
|August 1, 2019
Summary
Mathematical models for childhood diseases like measles require age structure. Incorporating age-specific transmission rates improves accuracy, especially during vaccination transitions, unlike simpler homogeneous models.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health
Background:
- Mathematical models have long been used to study childhood diseases.
- Homogeneous models accurately describe measles dynamics pre-vaccination, but struggle with post-vaccination periods.
Purpose of the Study:
- To investigate why standard epidemiological models fail to accurately capture disease dynamics after vaccine introduction.
- To compare the predictive power of homogeneous versus age-structured models for measles transmission.
Main Methods:
- Developed and compared homogeneous and age-structured mathematical models for disease transmission.
- Fitted these models to historical measles notification data from London, spanning the vaccine introduction era.
Main Results:
- Homogeneous and age-structured models show similar dynamics without vaccination.
- Age-structured models significantly outperform homogeneous models in fitting post-vaccination measles data.
- Immunization alters transmission rates and infection prevalence by age, impacting seasonality.
Conclusions:
- Age structure and contact heterogeneities are crucial for accurately modeling transient disease dynamics during secular trends.
- Age-structured models are essential for understanding and predicting infectious disease behavior in changing epidemiological landscapes.
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