Structured models of infectious disease: Inference with discrete data

Abstract

The usage of structured population models can make substantial contributions to public health, particularly for infections where clinical outcomes vary over age. There are three theoretical challenges in implementing such analyses: i) developing an appropriate framework that models both demographic and epidemiological transitions; ii) parameterizing the framework, where parameters may be based on data ranging from the biological course of infection, basic patterns of human demography, specific characteristics of population growth, and details of vaccination regimes implemented; and iii) evaluating public health strategies in the face of changing human demography. We illustrate the general approach by developing a model of rubella in Costa Rica. The demographic profile of this infection is a crucial aspect of its public health impact, and we use a transient perturbation analysis to explore the impact of changing human demography on immunization strategies implemented.