Key Words mathematical models, computer models, epidemiologic methods, system analysis, inference robustnesss Abstract Understanding what determines patterns of infection spread in populations is important for controlling infection transmission. The science that advances this understanding uses mathematical and computer models that vary from deterministic models of continuous populations to models of dynamically evolving contact networks between individuals. These provide insight, serve as scientific theories, help design studies, and help analyze data. The key to their use lies in assessing the robustness of inferences made using them to violation of their simplifying assumptions. This involves changing model forms from deterministic to stochastic and from compartmental to network, as well as adding realistic detail and changing parameter values. Currently inferences about infection transmission are often made using stratified rate or risk comparisons, logistic regression models, or proportionate hazards models that assume an absence of transmission. Robustness assessment will show many of these inferences to be wrong. A community of epidemiologist modelers is needed for effective robustness assessment.

Susser and Susser have analyzed epidemiology's past and find this discipline currently in transition from an era employing a "black box" paradigm to an era of "eco-epidemiology" with a new paradigm." 2 They admonish us to choose

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In order to assess the individual risk of acquiring sexually transmitted diseases (STDs), both characteristics of the partnership and the individual should be considered. Partnership characteristics have been used as risk markers for STD transmission but their distribution has not been well described. Using a self-administered questionnaire, we collected information on the partnership characteristics of the 4 most recent sexual partners of the members of 9 university women's social organizations at the University of Michigan. Respondents were asked to report the setting of the first meeting of partners, the length of the presexual relationship, condom use at the first sexual encounter and the total number of sexual encounters within that partnership. We graphically analyse changes in these partnership characteristics with respect to partnership order. As the number of sexual partners increased the women in this population were more likely to report partnership characteristics associated with an increased risk of acquiring an STD. In addition, partnership characteristics varied with the order of the partnership, implying that no single partnership is representative of all others.