In order to tackle the infeasibility of building mathematical models and conducting physical experiments for public health emergencies in a real world, we apply the ACP (Artificial societies, Computational experiments, and Parallel execution) approach to public health emergency management. We conducted a case study on the largest collective outbreak of H1N1 influenza at a Chinese university in 2009. We built an artificial society to reproduce H1N1 influenza outbreaks. In computational experiments, aiming to obtain comparable results with the real data, we applied the same intervention strategy as that was used during the real outbreak. Then we compared experiment results with real data to verify our models, including spatial models, population distribution, weighted social networks, contact patterns, students’ behaviors, and models of H1N1 influenza disease, in the artificial society. We then applied alternative intervention strategies to the artificial society. The simulation results suggested that alternative strategies controlled the outbreak of H1N1 influenza more effectively. Our models and their application to intervention strategy improvement show that the ACP approach is useful for public health emergency management
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