Evaluating the effectiveness of a public health prevention program is complex, particularly when the course from infection, health behavior, exposure, or genetic predisposition to disease spans multiple decades, when new activities are building upon existing interventions, and when resource constraints limit the range of reasonable choices. Specifically,

• It is often impossible to observe the complete course of chronic disease, and even the best available data are generally based on surrogate markers and intermediate endpoints.
• It is often not feasible to conduct randomized controlled trials of every potential preventive and treatment approach.
• The effects of public health interventions – such as vaccination -- may extend beyond individual patients and may not be measured easily from empiric data.
• The information required to develop policies and regulations requires the synthesis of data from many sources.
• The consequences and costs of interventions may have many facets (attributes), so that tradeoffs and value judgments about their relative importance are unavoidable.

Mathematical models can be useful tools in overcoming these challenges because they provide a formal framework for synthesizing data from multiple sources in an internally consistent and epidemiologically plausible way. These models combine information about natural history and interventions obtained from a wide variety of sources with other relevant demographic and epidemiological characteristics of the population under study. Such models also make it possible to extend information available in observational studies by extrapolating patterns beyond the time horizon of a single study. In addition to relating biological and clinical information, these models provide both qualitative and quantitative insight into the relative importance of different components of the prevention or treatment process and allow investigation of how varying key parameters will change results. By identifying the most influential variables, these models can be used to identify a range of feasible options for policy makers, highlight key information gaps for researchers, and help prioritize data from new studies.