Abstract
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.
Highlights
In recent years, public healthy events, such as SARS [1] and H1N1 [2], happen more and more frequently
This paper discusses how to build agent model in the aspects of agent state, the state transitions, and the social networks in a campus. These models are built according to the specification of finite state automata (FSA) [14]
Domain specific data, and survey from real world to build agent and environment model is crucial for the coherence with the real world
Summary
Public healthy events, such as SARS [1] and H1N1 [2], happen more and more frequently. Traditional ways for the research on epidemic transmission are used to build the SIS, SIR, and their extended models, say, SEIS, SEIRS, MSEIRS, and so forth [5]. GASM can simulate epidemic in a global scale with more than six billion agents while considering individual behaviors The advantages of these tools are as follows:. We propose a framework using agent-based modeling and simulation to build an artificial society [9]. Microbehaviors, like movement and talking, could be simulated to induce complex macroscopic phenomenon in the agent-based artificial society. Environment is another important model unit which serves as the container for the agent movements.
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