In its third era (the past few decades), epidemiology has moved away from its classical foundations in infectious diseases to focus on chronic diseases, and, of necessity, reverted to an emphasis on statistical identification of risk factors rather than elucidation of mechanisms and dynamics. Some leaders in the field rue this development. Dr. Mervin Susser, a highly respected statesman–epidemiologist, recently admonished his fellow epidemiologists to adopt a multilevel, systems analysis, “eco-epidemiology” approach (Susser and Susser, 1996a, 1996b). Others have argued that modern epidemiology must fully embrace new powerful computational technologies to analyze, model, and simulate the dynamics of disease generation and propagation (Koopman, 1996).

TOWARD A NEW SCIENCE OF EXPERIMENTAL EPIDEMIOLOGY

Epidemiology is not commonly considered to be an experimental science. The discipline concerns itself with large populations of ill (or potentially ill) humans, and rigorously controlled experimental designs are rarely practical or ethical. When prospective epidemiological studies are undertaken, as in Phase III efficacy trials of vaccines, study population size limitations are such that usually only one new intervention can be evaluated. Mathematical and statistical modeling is invaluable in the design and interpretation of epidemiological studies, but is not well suited to simulation of interventions and outcomes. Extraordinary increases in the speed and memory of inexpensive computer processors now make it possible to create and run computations that were impossible only a few years ago, giving rise to computational simulations of unimagined speed, granularity, and stochasticity. Such simulations could serve as dry “laboratories” for a new science of experimental epidemiology in which new population-level interventions could be designed, evaluated, and iteratively refined on simulated epidemics, with tangible benefits for real-world epidemic prevention and control efforts. Successful development of this new science will require interdisciplinary collaborations between epidemiologists and other computationally oriented academic disciplines (Levin et al., 1997). Indeed, some exciting developments have already appeared at these epidemiological/computational interfaces and are briefly reviewed here. They include harmonic decomposition, agent-based modeling, network modeling, and creation of digital organisms.

Harmonic Decomposition Analysis

The tempo, mode, and spatial distribution of an epidemic infectious disease are well understood to reflect external forcing events (e.g., weather or climate), host immune factors (e.g., herd immunity), microbial evolu-



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