Pandemic Preparedness Training for Schools (PTS)

DOI: 10.5176/978-981-08-5480-5_rp122

Authors: J. Eric Dietz, Julie Drifmeyer, Kara Leonard, Chih-hui Hsieh, Steven Dunlop

Abstract:

Pandemic Preparedness Training for Schools (PTS) is an evidence-based flu prevention program for schools. The purpose of this project is to provide an assessment of how policies, behaviors, and the environment might affect transmission of a pandemic influenza virus within a school. Following a thorough assessment of the school, specific recommendations for improvement are provided to the user. The results of the assessment are enhanced by a game-like simulation of the spread of the virus through the participating school, providing a visual representation of how students and staff might be affected by a pandemic influenza virus. The simulation applied for this program can model the relevant geographic areas with all the relevant features (schools, hospitals, railways, airports, lakes, rivers, and business districts) to create an artificial virtual community. These virtual geographies can be customized to mimic real geographies, such as all the counties within the state of Indiana. The epidemiological model utilized consists of two components: the emergent social network based on the locality, mobility, and the interactions of the artificial agent and the epidemiology of pandemic influenza that spreads through the social network. The propagation of the disease may be affected via artificial agent isolation or artificial agent behavior.The virtual community created in the simulation will have a virtual population represented by artificial agents. An artificial agent is able to represent the activity of a human through a combination of learned variables and interactions. Related research has shown that these artificial agents and their interactions with one another create an emergent social network (Wasserman & Faust, 1994). Like living beings, each artificial agent has different interactions and experiences, and thus acts differently when faced with a situation. Computational models of artificial agents’ attributes (like age, gender, health status, location, infection susceptibility and state of well-being) and behaviors (like mobility and social networking) were created to reinforce policy and preventive health training that reduce disease spread.