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大数据在突发公共卫生事件预测和监测中的算法优化

Algorithm Optimization of Big Data in Predicting and Monitoring Public Health Emergencies

Advance in Law / 2025,7(1): 93-104 / 2025-02-12 171 82

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• Authors: 翁心睿     
• Information:

  北京化工大学，北京

• Keywords: Big data; Epidemic monitoring; Risk prediction; Pulley system 大数据；疫情监测；风险预测；轮组模型
• Abstract: The complexity and unpredictability of public health emergencies pose significant challenges to traditional monitoring systems. This article proposes the “pulley system” model, leveraging big data technologies to achieve risk monitoring, information collection, analysis and evaluation, and predictive support, thereby enhancing decision-making efficiency and accuracy. To address issues in current data applications, such as difficulties in identifying misinformation, data silos, privacy breaches, and insufficient sustainability of technological innovation, optimization directions are proposed. These include strengthening data collection standards, improving data resource systems, ensuring the security of personal health data, and establishing long-term mechanisms for technological innovation. Furthermore, the article highlights the application of big data driven by scenario-specific needs, enabling real-time integration and analysis of multi-source data to advance the intelligence and automation of epidemic prediction and monitoring. By summarizing practical experiences and theoretical models, this article provides a systematic approach to empowering public health emergency monitoring with big data, offering insights for innovation in risk governance and emergency management. 突发公共卫生事件具有复杂性和不可预测性，这对传统监测体系构成了巨大挑战。文章提出“轮组”模型，借助大数据技术达成风险监测、信息采集、分析研判及预测支持，提升决策效率和精准性。针对当前数据应用中存在的虚假信息难辨、信息孤岛、隐私泄露及技术创新持续性不足等问题，提出优化方向，包括强化数据采集标准、完善数据资源体系、保护个人健康数据安全及建立长效技术创新机制等。此外，文章强调场景需求驱动下的大数据应用，可实现对多源数据的实时整合和分析，推动疫情预测、监测的智能化和自动化。通过总结实践经验与理论模型，文章为大数据赋能公共卫生事件监测提供了系统性思路，为风险治理和应急管理的创新提供了参考。
• DOI: https://doi.org/10.35534/al.0701009
• Cite: 翁心睿．大数据在突发公共卫生事件预测和监测中的算法优化［J］．法学进展，2025，7（1）：93-104.