Ever since the November 2002 - July 2003 SARS outbreak, epidemiologists have tried to refine the use of computer simulations to help public policy decision-makers understand the real world dynamics of epidemic transmission and to assess the potential efficacies of various public health policies. Here we describe our attempt to help novice researchers understand epidemic dynamics with the help of Huang et al.'s (2004) Cellular Automata with Social Mirror Identity Model (CASMIM), a small-world epidemiological simulation system. We designed three sets of instructional experiments to test our assumptions regarding a) simulating epidemic transmission dynamics and associated public health policies; b) assisting with understanding the properties and efficacies of various public health policies; c) constructing an effective, low-cost (in social and financial terms) and executable suite of epidemic prevention strategies; and d) 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 exploring applicable combinations of prevention or suppression strategies.
|Number of pages||6|
|Journal||WSEAS Transactions on Information Science and Applications|
|State||Published - 1 May 2006|
- Epidemiological model
- Learning through simulation
- Public health policy
- Small-world network