An ACP Approach to Public Health Emergency Management: Using a Campus Outbreak of H1N1 Influenza as a Case Study

Abstract

In order to tackle the infeasibility of building mathematical models and conducting physical experiments for public health emergencies in the real world, we apply the Artificial societies, Computational experiments, and Parallel execution (ACP) approach to public health emergency management. We use the largest collective outbreak of H1N1 influenza at a Chinese university in 2009 as a case study. We build an artificial society to simulate the outbreak at the university. In computational experiments, aiming to obtain comparable results with the real data, we apply the same intervention strategy as that was used during the real outbreak. Then, we compare 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. In the phase of parallel execution, alternative intervention strategies are proposed to control 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.