毕业生知识库

现实世界中的许多传播现象均可抽象成复杂网络上的传播过程，例如，网络空间中基于社交网络的信息传播、物理空间中基于人群接触网络的疾病传播、社会空间中基于人群影响网络的行为传播等。复杂网络上的传播动力学建模及情景分析可以探究信息、疾病等在内的复杂传播机制，为舆论和疾病的防控等提供决策依据。但复杂网络上的传播动力学研究仍存在诸多困难和挑战。随着互联网、交通网等技术的大力发展，信息和疾病的传播都呈现出复杂多样的新模式，并主要表现为在信息物理社会空间中信息-疾病的耦合传播。例如，我们正在经历的新型冠状病毒肺炎防控阶段，网络信息成为人群获取与疾病相关知识的主要来源，并成为疫情传播的“触媒”，正面消息可以提高防控意识并采取积极有效的保护措施，从而抑制疫情的传播。疾病相关信息的传播对流行病大爆发产生的影响不可忽视。如何对这些复杂的异质性传播现象进行建模和干预分析是当前的研究难点。
受信息物理社会系统框架的启发，本论文旨在研究复杂网络下受信息、行为影响的疫情传播机制和新型冠状病毒肺炎背景下的“情景-应对”型疫情应急决策，为采取措施的方案、时机和力度等重要应急决策问题提供参考。该课题针对当今世界所面临且迫切需要解决的全球性疫情问题，在公共卫生应急管理、社交媒体分析等领域中具有重要研究意义。
本论文分别针对信息空间的信息传播、物理空间的疾病传播、以及信息-物理空间中的耦合传播展开了深入研究，本论文的主要贡献与创新点包括：
(1) 首先针对信息空间的线上信息传播机制进行研究。信息在网络上的传播是一个多因素决定的过程，具有差异性、随机性和复杂性，已有实证研究对网络信息传播的差异性和复杂性进行分析，但从建模的角度对其特征进行理论解释的研究较少。为捕捉在线信息传播机制的复杂模式，本项工作考虑到网络信息传播与个人心理素质密切相关，受到心理学理论的启发及对传播特性的了解，提出一种基于注意力衰减理论和干扰说的线性阻力阈值（RLT）传播模型。随后，验证RLT 模型在复杂网络上的传播动力学行为，量化比较唯一用户数、级联规模、级联深度、级联宽度四类传播特性并探究网络结构和模型参数对信息传播的影响。最后，通过情景建模，分析模拟禽流感、COVID-19 大流行的网络舆情传播情况，验证模型的有效性并在网络信息投放、谣言和舆情控制等方面给出建议。
(2) 其次针对物理空间中的疾病传播机制进行研究。在进行传染病建模及防控策略分析时，研究者们更侧重于基于大尺度人流迁移模式的流行病传播及策略评估，而忽略了社区内传播，尤其是社区内接触模式的构建。为构建更具有现实性和符合COVID-19 早期传播特性及中国国情的传播场景，首先根据中国社区接触模式提出社区异构分层网络。其次，充分研究COVID-19 在中国的传播特性及流行病学特征，考虑到无症状患者，确诊延迟等现状，提出基于社区异构分层网络的易感-暴露-感染-无症状-康复-住院报道-死亡（SEINRHD）传播模型。利用武汉早期数据及实证研究综述确定流行病学参数。通过情景建模评估无症状病例追踪率，诊断延迟时间和策略实施时机对流行病进展的影响。最后，利用该模型进行案例分析，讨论疫苗接种对COVID-19 流行的影响。
(3) 最后针对信息-物理空间中的耦合传播机制进行研究。目前更多的研究都将重点放在物理空间流行病本身的传播上，而忽略了信息空间有关疾病信息的传播而带来的影响。已有研究中通常需要进行大量理想化假设，且是在系统级别上对交互进行描述，而对于个体属性的异质性和行为的复杂性难以捕捉，这样的网络缺乏了复杂性和现实性。本项工作针对信息扩散—行为改变—疾病传播这一过程进行建模，考虑了信息、行为、疾病在各层网络上的独立传播和耦合机制，提出了基于多层复杂异质网络的信息-行为-疾病传播（IBDN）模型。随后，结合当前COVID-19 奥密克戎变异毒株传播特性及现状参数化模型，并通过情景建模分析来评估网络传播参数对疫情的软干预效果。最后，进行不同疾病基本再生数下的敏感性分析，综合实验结论针对疫苗接种、开放政策等防控措施提出合理的建议。

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.