A growing number of epidemiologists are now working to refine computer simulation methods for diseases as a strategy for helping public policy decision-makers assess the potential efficacies of tactics in response to newly emerging epidemics. These efforts spiked after the SARS outbreak of 2002– 2003. Here we describe our attempt to help novice researchers understand epidemic dynamics with the help of the cellular automata with social mirror identity model (CASMIM), a small-world epidemiological simulation system created by Huang et al. in 2004. Using the SARS scenario as a teaching example, we designed three sets of instructional experiments to test our assumptions regarding (i) simulating epidemic transmission dynamics and associated public health policies, (ii) assisting with understanding the properties and efficacies of various public health policies, (iii) constructing an effective, low-cost (in social and financial terms) and executable suite of epidemic prevention strategies, and (iv) reducing the difficulties and costs associated with learning epidemiological concepts. With the aid of the proposed simulation tool, novice researchers can create various scenarios for discovering epidemic dynamics and for exploring applicable combinations of prevention or suppression strategies. Results from an evaluative test indicate a significant improvement in the ability of a group of college students with little experience in epidemiology to understand epidemiological concepts.
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