The use of logic models and simulation modeling are among the techniques recommended by the Centers for Disease Control and Prevention (CDC) for gathering and analyzing evidence for the purposes of planning and evaluating public health policies, intervention strategies, and programs. However, in most countries, logic models are much more commonly used than simulation modeling, and the capacity for computational modeling is a rare luxury. Most public health professionals recognize that static models, regardless of how good the logic or how detailed the mathematics of the simulation, may nevertheless be severely constrained or weakened if they can not provide information about the impact of dynamic changes and secular trends in important parameters. The chapter by Seitz and Hulin is a well-documented argument in favor of increased exploration, and greater use, of computational modeling in social and behavioral research, including program evaluation. The authors make their argument by providing an extended case analysis of HIV transmission, because this global health problem has both multi-level causal components and their feedback over time changes the transmission dynamics. Using computational modeling methods, including “counterfactuals” and “process decomposition”, to exploit alternative scenarios for prevention and control of HIV/AIDS, Seitz and Hulin make an important contribution by focusing the attention of public health experts and others on the strengths and limits of current methods of evaluation research and analysis. They have succeeded in drawing our critical attention to computational modeling as an emergent research paradigm.
Bernstein, R.S. (2002), "Multi-level simulation analysis: A methodology for planning and evaluation in public health", Yammarino, F.J. and Dansereau, F. (Ed.) The many faces of multi-level issues (Research in Multi-Level Issues, Vol. 1), Emerald Group Publishing Limited, Bingley, pp. 381-386. https://doi.org/10.1016/S1475-9144(02)01043-3
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