The authors of this paper advocate for the expanded use of mathematical models in epidemiology and provide an overview of the principles of mathematical modeling. Mathematical models can be used throughout the epidemiological research process. Initially they may help to refine study questions by visually expressing complex systems, directing literature searches, and identifying sensitive variables. In the study design phase, models can be used to test sampling strategies, to estimate sample size and power, and to predict outcomes for studies impractical due to time or ethical considerations. Once data are collected, models can assist in the interpretation of results, the exploration of causal pathways, and the combined analysis of data from multiple sources. Finally, models are commonly used in the process of applying research findings to public health practice by estimating population risk, predicting the effects of interventions, and contributing to the evaluation of ongoing programs. Mathematical modeling has the potential to make significant contributions to the field of epidemiology by enhancing the research process, serving as a tool for communicating findings to policymakers, and fostering interdisciplinary collaboration.

Although these US 2000 Census-weighted estimates of HSV-1 and HSV-2 seroincidence apply only to young, military adults, they nonetheless supply, to our knowledge, the only national figures that might be used to predict US national HSV1/2 seroincidence in young adults. Thus, we believe that our findings in this military population can be used to inform the planning of HSV-1 and 2 prevention measures in the general, young-adult US population.

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