基于场景干预的重大传染病跨时空传播的局部风险预测模型研究

大城市中新发重大传染病的突发事件是公共卫生安全隐患的重要来源，全球化背景下，人口越来越密集，突发传染病流行风险日益加剧。本研究的目的是掌握新发重大传染病的传播扩散规律、聚集性特征、潜在干预策略效果等，为制定高效应急反应预案提供科学依据。拟通过回顾和重构新发重要传染病队列，掌握传染病网络拓扑结构形态与动态参数，建立新发重大传染病（SARS，COVID-19等）传播的SEIR基础模型，研究模型动力学稳定性态、疫情稳定和暴发的阈值条件；基于复杂网络理论，模拟和分析人口流动及城市化对新发传染病发生和传播的作用；分析不同防控策略对重大新发传染病感染的影响；通过干预情景假定，模拟干预措施及策略场景下传染病的流行发展过程，从而对不同干预措施的效果做出定量评价。本研究为复杂网络理论下场景模拟及传染病队列与数学模型的结合研究，可为将来新发传染病发生时，及时进行预测和防控，及时调配医疗资源，提供一定证据。

New major infectious diseases are an important source of public health security risks in urban emergencies. Under the background of urbanization and globalization, the epidemic risk of new infectious diseases is increasing. The purpose of this study is to understand the transmission mode, aggregation characteristics and spatial risk factors of new major infectious diseases such as COVID-19, so as to provide scientific basis for the formulation of an efficient emergency response plan. The specific research contents include reviewing and reconstructing the cohort of important new infectious diseases, determining the topology and dynamic parameters of the network concept, establishing a basic model for the spread of new major infectious diseases (SARS, COVID-19, etc.), and studying the dynamic stability of the model state, study the threshold conditions for the stability of the epidemic, construct a multi-city mixed model nationally and globally, and simulate and analyze the effects of population movement and urbanization on the occurrence and spread of new infectious diseases. This study will construct a SEIR model that includes different outcome stages and different strategies for patients with infectious diseases, analyze the impact of different strategies on major new infectious disease infections in cities, and simulate the epidemic process of infectious diseases under various interventions through the quantitative evaluation of the effects of interventions. This study is a combination of scenario simulation and infectious disease queue and mathematical model under the complex network theory. It can provide evidence for the prediction and prevention of new infectious diseases in the future, and timely allocation of medical resources.