【摘要】 针对非常规医院感染暴发事件的高度复杂性与深度不确定性，为突破传统应急管理模式中被动响应、预案固化等局限性，系统构建适用于非常规医院感染暴发事件的“情景-应对”应急管理范式，提升应对深度不确定危机的科学化与前瞻性能力。文章借鉴国家自然科学基金委“非常规突发事件应急管理”重大研究计划的理论成果，融合情景构建、动态推演与应急决策理论，提出一套涵盖概念界定、要素识别、网络建模与决策映射的全周期研究路径。构建了以“概念—情景—推演—决策”为主线的“情景-应对”范式框架：首先，厘清非常规医院感染暴发事件的核心属性与演化机理；其次，提取关键情景要素并构建其关联网络；进而，实现从静态结构到动态演化的多路径推演；最后，建立基于情景特征的自适应应急决策匹配机制。该范式实现了从被动响应向主动预演的转变，为医院感染应急管理体系的智能化升级提供系统的理论支撑与实践路径参考。更多还原

【Abstract】 In view of the high complexity and deep uncertainty of unconventional hospital infection outbreak events, in order to break through the limitations of passive response and rigid contingency plans in the traditional emergency management model, a "scenario-response" emergency management paradigm suitable for unconventional hospital infection outbreak events is systematically constructed to enhance the scientific and forward looking ability to deal with deeply uncertain crises. Drawing on the theoretical achievements of the major research program of "Emergency Management of Unconventional Emergencies" of the National Natural Science Foundation of China, and integrating scenario construction, dynamic deduction and emergency decision making theories, a full cycle research path covering concept definition, element identification, network modeling and decision making mapping is proposed. A "scenarioresponse" paradigm framework with the main line of "concept-scenario-deduction-decision" is constructed. First, the core attributes and evolution mechanism of unconventional hospital infection outbreak events are clarified. Second, key scenario elements are extracted and their associated network is constructed. Then, multi-path deduction from static structure to dynamic evolution is realized. Finally, an adaptive emergency decision making matching mechanism based on scenario characteristics is established. This paradigm realizes the transformation from passive response to active rehearsal, and provides systematic theoretical support and practical path reference for the intelligent upgrading of the hospital infection emergency management system.更多还原