Transmission dynamics models are capable of providing a valuable tool with which to investigate the epidemiology of infectious diseases. The process of simplification of the real world inherent in the design and implementation of such a model is of itself an important step in achieving epidemiological understanding; indeed, in contrast to so-called black box models in which the relative importance of interactions giving rise to results remains obscure, it has often been said that mathematical models are tools for thinking about epidemiology in a precise way and are, in a sense, thought experiments.

The ability of the modeling framework to include population age structure and age-specific parameters allows exploration of many facets of infection in the context of age and aging. An appreciation of the theoretical abilities of such models, however, should not distract from the fundamental importance of having sufficient good data with which to parameterize the models and against which to measure model performance. The modeling process itself can help to highlight what data are needed to further epidemiological understanding and to assess prospects for control, but a lack of reliable data inevitably limits in turn the reliability of further modeling work, although sensitivity and uncertainty analysis are useful tools for assessing how much faith may be placed in the results being produced.

Among the areas where good data are often lacking are the quantification of risk of infection per contact, the nature of contact patterns, and how these change over time. A further challenge lies in establishing and understanding the importance of heterogeneity in behaviors and contact patterns. In the field of sexually transmitted infections, behavioral data are vital but particularly difficult to acquire and validate. Insights from the field of genetics have the potential to greatly increase our understanding of variation in responses to infection and differing patterns of disease, but also to increase the complexity and to bring new challenges for modelers. Infections with multiple strains, reassortment of genetic material between strains, and significant cross-immunity between strains pose yet another challenge. Changing demography, including migrational trends, has the potential to profoundly influence the epidemiology of many infections but also to substantially increase the difficulty of the task of specifying and acquiring the necessary data for modeling.

As far as the techniques and approaches used in infectious disease modeling are concerned, historically these have been adapted from uses in other fields, primarily the physical sciences, and the introduction of new techniques often occurs when those entering infectious disease modeling from another field bring with them new insights and approaches to modeling from that field. With continually increasing computing power and no lack of potentially useful modeling techniques, given sufficient suitable and reliable data, the major challenge remains that of achieving a satisfactory balance between simplicity and explanatory power and that of understanding and interpreting model results.

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