Structured models of infectious disease: inference with discrete data

Area of Science:

• Public Health
• Epidemiology
• Mathematical Modeling

Background:

• Clinical outcomes of infections often vary significantly with age.
• Structured population models offer a framework for analyzing demographic and epidemiological transitions.
• Implementing these models involves challenges in framework development, parameterization, and evaluating strategies under changing demographics.

Purpose of the Study:

• To present a general approach for implementing structured population models in public health.
• To develop and illustrate a model for rubella, considering its age-specific demographic profile.
• To analyze the impact of changing human demography on immunization strategies.

Main Methods:

• Development of a theoretical framework integrating demographic and epidemiological transitions.
• Parameterization of the model using data on infection biology, demography, population growth, and vaccination.
• Application of transient perturbation analysis to assess demographic influences on immunization.

Main Results:

• A structured population model for rubella was developed and applied to Costa Rica.
• The model demonstrated the crucial role of the demographic profile in public health impact.
• Transient perturbation analysis revealed how demographic changes affect immunization strategy effectiveness.

Conclusions:

• Structured population models are valuable tools for public health, particularly for age-structured infectious diseases.
• The developed framework effectively integrates demographic and epidemiological factors.
• Understanding demographic shifts is essential for optimizing public health interventions and immunization programs.