基于数字孪生技术的多重耐药菌传播路径建模与仿真研究
Modeling and simulation study of transmission pathways of multidrug-resistant organisms based on digital twin technology
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摘要:目的 探讨数字孪生技术在多重耐药菌(MDROs)院内传播路径建模与仿真中的应用, 提出基于数字孪生的MDROs传播防控策略。方法 对2023年6月-2024年6月医院实时数据与人员流动信息进行采集与分析, 通过医院环境的数字化建模与三维仿真技术, 建立医院内部环境和MDROs传播的动态模型。利用模拟平台, 分析不同防控措施对MDROs传播风险的影响, 采用数字孪生技术进行细菌传播路径的预测与实时监测。结果 仿真数据显示, 在原始防控条件下, 病房A的感染人数为8人, 传播时间为12 d; 在实施"提高清洁频率+加强人员防护"的综合措施后, 感染人数减少至4人, 传播时间缩短至6 d, 传播风险减少率达到45.00%。数字孪生模型能有效反映医院MDROs的传播规律, 提供不同防控策略下的量化风险评估。结论 数字孪生技术能实时预测和模拟细菌传播路径, 为医院提供科学的决策支持, 有望成为MDROs防控的新范式。Abstract:OBJECTIVE To explore the application of digital twin technology in modeling and simulating the hospital-associated transmission pathways of multidrug-resistant organisms (MDROs) and propose MDRO transmission prevention and control strategies based on digital twins.METHODS Real-time hospital data and personnel mobility information from Jun.2023 to Jun.2024 were collected and analyzed.A dynamic model of the hospital′s internal environment and MDROs transmission was established by digital modeling and 3D simulation technology of the hospital environment.A simulation platform was utilized to analyze the impact of different prevention and control measures on MDROs transmission risks.Digital twin technology was employed for predicting and real-time monitoring of organism transmission pathways.RESULTS Simulation data showed that under initial prevention and control conditions, Ward A had 8 infected individuals and a transmission duration of 12 days.After implementing comprehensive measures of "increased cleaning frequency+enhanced personnel protection", the number of infected individuals reduced to 4, the transmission duration shortened to 6 days and the transmission risk reduction rate reached 45.00%.The digital twin model can effectively reflect the transmission patterns of MDROs within the hospital and provide quantitative risk assessments under different prevention and control strategies.CONCLUSIONS Digital twin technology can predict and simulate organism transmission pathways in real-time, providing scientific decision-making support for hospitals.It has the potential to become a new paradigm for MDROs prevention and control.