title: The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing.
abstract: Ebola virus disease (EVD) distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals' behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals' behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.
authors.affiliation: College of Information and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: College of Information and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Chen
authors.affiliation: College of Information and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Ma
authors.affiliation: College of Information and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Li
authors.affiliation: College of Information and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Zhichao
authors.initials: Z
authors.lastname: Song
authors.affiliation: College of Information and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Duan
authors.affiliation: College of Information and Management, National University of Defense Technology, Changsha 410073, China.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Qiu
mesh_terms: D000068476:Beijing; D004196:Disease Outbreaks; D019142:Hemorrhagic Fever, Ebola / Q000175:diagnosis* / Q000453:epidemiology* / Q000517:prevention & control; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D011237:Predictive Value of Tests