A Simulation Study on Large-scale Agent-based Epidemic Prediction and Control
With the increasing availability of powerful computing resources(e.g.,GPU computing,supercomputing and cloud computing),the research interest of agent-based models is shifting to large-scale social systems.Among these systems,epidemic spread on a large scale(city/country/global)has remained a research focus for a long period.This paper presented a simulation study on large-scale epidemic prediction and control using agent-based models.A case study is conducted with the data generated for the city of Beijing.To test the quality and explore the possibility of the implemented large-scale agent-based model for epidemic prediction and control(H1N1),different simulation scenarios were conducted and simulation results were analyzed.The study presented in this paper performs well in terms of testing different scenarios and combining scenarios is extremely easy.In addition,shifting behavior of agents as a result of scenarios or changing compliance with government measures,e.g.,decreasing compliance over time,or increasing compliance with an increasing number of infections,can be easily modeled as well.
Agent-based Models Mega-city Epidemic Prediction and Control
Mingxin Zhang Alexander Verbraeck Xiaogang Qiu
College of Politics,National Defense University,Shanghai 201601,P.R.China Faculty of Technology,Policy and Management,Delft University of Technology,The Netherlands College of Information Systems and Management,National University of Defense Technology,Changsha 410
国内会议
厦门
英文
74-81
2017-11-17（万方平台首次上网日期，不代表论文的发表时间）