Study On Modeling And Simulation Of Virus Propagation Based On Complex Agent Networks

Virus infections obviously harm human health and even threat human life. The prevalenceof infectious diseases caused by viruses can arouse social panic and disturbance, andin the meantime cost large amount of resources for prevention, containment, diagnosesand treatment. Apart from the study of natural infectious diseases, the study of bioterroristicattacks and their subsequent infectious diseases becomes an important field, inwhich the precaution of bioterrorism and crisis decision making are of great internationalconcerns.Modeling and Simulation is an effective approach to mimic and study virus propagation,due to infectious diseases'non-experimentation among human in the real world. Theaim of this thesis is to present a method for describing the characteristics and dynamicsof virus propagation and characterizing the complex interactions among individuals bygenerating virtual complex networks so as to support stochastic simulations. Afterwards,we can predict the trends of infectious diseases by analyzing simulation results, and thensupport public health policy making based on features of viruses concerned.The paper presents a novel Complex Agent Networks method, which is a comprehensivesolution to the modeling at individual and population scales, based on deep investigationson three common epidemic modeling methods, i.e., traditional equation-basedmodeling, agent-based modeling and complex-network-based modeling. Our ComplexAgent Networks method can seamlessly integrate Multi-agent Systems and complex networks,utilizing the merits of both methods while avoiding their demerits. This methodcan generally guide the overall modeling at individual and population scales.Based on the utilization of the Complex Agent Networks method to analyze the dynamicvirus propagation procedure, we construct a statistical model for assessing the epidemiologicalimpact of virus propagation in simulations. Then we analyze the influentialfactors of virus propagation on different scales. On population scale, the macro characterizationof the complex interactions between hosts, intervention policies of infectiousdiseases and demographical influences are studied. On individual scale, hosts'individualagent modeling, individual properties and behavior related to infection progression andparticularly the probabilistic model of virus infection caused by interactions are studied.The two interacting factors that influence individual decision-making for avoiding infections are emotion and cognition. A decision framework is constructed based on thestudy of the relationship between the two factors and their roles in the decision-making.Guided by this framework, a Fussy-Cognitive-Map-based representation model of individualemotions and cognition which supports unsupervised learning is presented, withprimary, secondary and senior emotions and cognitive knowledge of infectious diseasesconsidered. Initial causal influence weights between each pair of concepts can be tuned byapplying Nonlinear Hebbian Learning. We can set individual decision rules for avoidinginfections mapping to the iterated values of concepts in a fussy cognitive map, accordingto people's reactions in reality when they are faced with infectious disease spreading.Then a key algorithm, named configuration model based scale-free complex networkgenerations, is presented to support rapid flexible and robust generations of complex networkswith given size and power law degree distributions. The social networks for viruspropagation can be generated based on the algorithm, with local principles of forming interactionsamong individuals considered, and interaction properties assigned to edges. Inaddition, random addition and deletion of vertices and edges required by other algorithmscan be avoided in our algorithm, and thus the infection progression information of eachindividual can be retained to guarantee agents'autonomy and independence.Next, a virus propagation simulation prototype is designed and implemented basedon aforementioned study and two applications are built to illustrate the prototype. One isfor simulating the human swine influenza A [H1N1] propagation in a closed universityarea, the other is for simulating the HIV epidemic among men who sex with men in Amsterdam,the Netherlands. After performing different scenario simulations, suggestions forpublic health decision making are given to hold back these two different infectious diseases.These two applications are advantageous to validate the Complex Agent Networksmethod presented previously.This study can effectively support the modeling and simulation of virus propagation,and provides a general reference procedure and framework for mimicking virus propagationin the real world. Quantitative analyses of the spreading of infectious diseases,conducted based on this study, helps theoretically and practically to investigate the interdictionsof both naturally occurring and bioterroristically aroused infectious diseases.