A modeling and experiment framework for the emergency management in AHC transmission

Emergency management is crucial to finding effective ways to minimize or even eliminate the damage of emergent events, but there still exists no quantified method to study the events by computation. Statistical algorithms, such as susceptible-infected-recovered (SIR) models on epidemic transmission, ignore many details, thus always influencing the spread of emergent events. In this paper, we first propose an agent-based modeling and experiment framework to model the real world with the emergent events. The model of the real world is called artificial society, which is composed of agent model, agent activity model, and environment model, and it employs finite state automata (FSA) as its modeling paradigm. An artificial campus, on which a series of experiments are done to analyze the key factors of the acute hemorrhagic conjunctivitis (AHC) transmission, is then constructed to illustrate how our method works on the emergency management. Intervention measures and optional configurations (such as the isolation period) of them for the emergency management are also given through the evaluations in these experiments.