|Thesis Advisor||曾大军 ; 曹志冬|
|Place of Conferral||中国科学院自动化研究所|
|Keyword||异质性复杂网络 传染病传播模型 信息传播 情景建模 新型冠状病毒肺炎|
Many transmission phenomena in the real world can be abstracted into transmission processes on complex networks, for example, information transmission based on social networks in cyberspace, disease transmission based on human contact with network in physical space, and behavior transmission based on human influence on network in social space. Transmission dynamic modeling and scenario analysis on complex networks can be deployed to explore complex transmission mechanisms such as information and disease. Moreover, they also provide decision-making basis for public opinion and disease prevention and control. However, challenges still persist in the previous study of propagation dynamics on complex networks. With the development of Internet, transportation network and other technologies, the transmission of information and disease presents complex and diverse new modes, which are mainly manifested as the coupling transmission of information and disease in the Cyber-Physical-Social space. For example, in the COVID-19 prevention and control phase, online information has become the main source of disease-related knowledge and the catalyst for the spread of the epidemic. Positive information can raise awareness of prevention and control with the active willingness of receiving vaccination to curb the spread of the epidemic. Accordingly, the impact of the dissemination of disease-related information on pandemics cannot be ignored. How to conduct modeling and intervention analysis of these complex heterogeneous propagation phenomena remains a technique challenge, which should be properly considered and addressed.
Inspired by the framework of Cyber-Physical-Social Systems, this paper studies the epidemic transmission mechanism influenced by information and behavior in complex networks and the “scenario-response” epidemic emergency decision-making in the context of COVID-19. We aim to provide a practical guide for important emergency decision-making issues such as the plan, timing and intensity of measures to be taken. This topic has valuable research significance in public health emergency management and social media analysis, targeting the urgent global epidemic problem.
This paper has carried out in-depth studies on information transmission in cyberspace, disease transmission in physical space, and coupled transmission in network-physical space. The main contributions and innovations of this paper include:
(1) First, the mechanism of information transmission in cyber space is studied. The dissemination of information in cyberspace is a process determined by multiple factors, with differences, randomness, and complexity. Existing empirical studies have analyzed the differences and complexity of network information dissemination. But few studies have theoretically explained its characteristics from the perspective of modeling. In order to capture the complex pattern of information transmission mechanisms in cyberspace, this work considers that network information transmission is closely related to individual psychological quality. Inspired by psychological theories and understanding of transmission characteristics, a linear resistance threshold (RLT) transmission model is proposed based on attentional attenuation theory and interference theory. Then, the propagation dynamics behavior of RLT model on complex networks is verified, the four types of propagation characteristics including unique users, cascade size, cascade depth, and cascade breadth are quantitatively compared, and the influence of network structure and model parameters on information transmission is explored. Finally, scenario modeling was used to analyze the spread of online public opinion simulating the avian flu and COVID-19 pandemic to verify the effectiveness of the model. And the suggestions on network information release and public opinion control have been given.
(2) Second, the mechanism of disease transmission in physical space is studied. When modeling infectious diseases and analyzing prevention and control strategies, researchers focused on epidemic transmission and strategy assessment based on large-scale human migration patterns but ignored intra-community transmission, especially the construction of intra-community contact patterns. In order to build a more realistic transmission scenario that is in line with the characteristics of early COVID-19 transmission and China’s conditions, a heterogeneous hierarchical network of communities is proposed based on the Chinese community contact model. Next, the transmission characteristics and epidemiological characteristics of COVID-19 in China should be fully studied. Considering asymptomatic patients and delayed diagnosis, the Susceptible-Exposed-Infectious-No symptoms-Recovered-Hospitalized and reported-Death (SEINRHD) transmission model based on a heterogeneous hierarchical network of communities is proposed. The epidemiological parameters were determined based on the early data in Wuhan and empirical research review. Scenario modeling was used to assess the impact of asymptomatic case tracing rate, diagnostic delay time, and timing of strategy implementation on epidemic progression. Finally, a case study was conducted to discuss the impact of vaccination on the COVID-19 epidemic.
(3) Finally, the coupling propagation mechanism in the cyber-physical space is studied. Currently, research has focused on the spread of epidemics in physical space, yet ignoring the impact of the spread of information about diseases in cyberspace. Existing studies usually require a large number of idealized assumptions and describe interactions at the system level. However, it is difficult to capture the heterogeneity of individual attributes and the complexity and individuality of behaviors. Such networks lack complexity and realism. In this work, we modeled the process of information diffusion, behavior change, and disease transmission. Considering the independent transmission and coupling mechanism of information, behavior, and disease in each layer network, we proposed an information-behavior-disease transmission (IBDN) model based on the complex heterogeneous network. Then, combined with the current parametric model of transmission characteristics and the current situation of COVID-19 omicron variant strain, the soft intervention effect of network transmission parameters on the epidemic was evaluated through scenario modeling analysis. Finally, the sensitivity of different diseases under the basic regeneration number was analyzed, and reasonable policy suggestions were given based on the experimental results.
|罗天怡. 复杂网络中的异质性传播动力学分析与情景建模[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
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