FIGURE E-1 Continuous wavelet transform decomposition of 1928–1964 Baltimore measles time series data showing that the incidence curve is decomposable into a shorter component with a periodicity of 12 months, and a longer component with a variable periodicity of 24–36 months. The longer component correlates closely with changes in birth rates.

tion, and population-level control efforts, as well as the complex dynamic interplay among these factors. Recently, new analytic techniques borrowed from physics, such as Fourier analysis and wavelet analysis, have permitted the decomposition of temporo-spatial epidemic harmonics (the aggregate signal) into modes, each of which may reflect only one of the underlying factors. For example, Fourier analysis has been used to decompose dengue and malaria data sets to reveal the weather-independence of interepidemic variability (Rogers et al., 2002; Hay et al., 2000). Of special interest is demonstration of the power of wavelet analysis to decompose measles epidemic harmonics to reveal recurrent spatial spreading patterns not evident in the undecomposed epidemic data. Such preliminary successes with decompositional techniques suggest they will make it possible to analyze and explain the dynamics of many infectious diseases (Grenfell et al., 2001; Strebel and Cochi, 2001).

Agent-Based Models

Agent-based computational models are computer programs in which a population of individual entities is created, and each individual is endowed with simple rules for interactions with the environment and with other individuals (Holland, 1995). Agents are typically programmed as two-dimensional entities that are distributed across a two-dimensional surface in proximity to other similar agents (but there is no inherent limit on this dimensionality). As the model runs, agents move over the surface and inter-

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