A major activity of both academic and governmental epidemiologists involves ascertaining the environmental contaminations, personal behaviors, biological factors, and other risk factors whose control will lead to the control of disease risks. Almost always the data analytic models used in this task are consistent with linear models of the causal process leading to disease. One of their basic assumptions is thus that the outcomes in one study subject are independent of the outcomes in other study subjects.

Infection transmission between humans is inconsistent with these linear model forms. Some particular consequences of this inconsistency explain why epidemiologists have been so unsuccessful in defining the modes of transmission of many infectious agents. Model inconsistencies also explain the poor performance of epidemiological methods in determining the relative importance of different factors contributing to infection risk at both an individual and a population level. It will be demonstrated how most of the effect of risk factors which increase transmission risk will not be detected by the usual analytic methods of epidemiology. This inadequacy of standard methods occurs even when contact patterns are random. It will be further demonstrated how non-random contact patterns can create additional difficulties for the detection of secondary risk factors.

Standard analytic models in epidemiology assume that causal actions occur directly on individuals. Thus their basic parameters are unlike those in transmission models which relate to interactions between individuals. The paradigm jump from cause acting directly on individuals to cause acting on interactions between individuals is a big one for epidemiologists. The paradigm jump would be facilitated if a data collection and analysis framework existed based on transmission models. Some approaches to developing that framework will be discussed.