大数据驱动的公共卫生风险监测:理论框架与实践反思

Big Data-Driven Public Health Risk Surveillance:Theoretical Framework and Practical Reflection

大数据驱动的公共卫生风险监测已成为公共卫生风险治理最为活跃的研究领域,但现有经验证据表明大数据监测的实践效果还不甚理想。本文从大数据治理和风险监测互动关系视角构建了大数据驱动公共卫生风险监测的理论框架,从数据来源、参与主体、模型算法、监测系统以及全球合作网络等方面梳理了大数据在公共卫生风险监测领域的应用进展,从风险属性、技术标准、区域经济差异以及参与者意识和能力等方面归纳了其所面临的现实困境。未来学界与实务界需要围绕公共卫生大数据从探索理论范式、构建互信关系、优化监测机制、探索循证决策以及整合监测系统等方面予以努力,共同推动大数据驱动的公共卫生风险监测这一新的实践模式的发展。在全球疫情防控依然严峻之际,本文有利于提升学术界对大数据驱动的公共卫生风险监测形成更清晰的经验认识。

Big data-driven public health risk surveillance has become one of the most active research fields of public health governance.However,the existing empirical evidence shows that the practical effect of big data surveillance is still insufficient.From the perspective of interactions between big data governance and risk surveillance,this paper aims to construct a theoretical framework of big data-driven public health risk surveillance.It reviews the progress of big data application in public health risk surveillance from the aspects of data sources,participants,model algorithms,surveillance systems and global cooperation networks.Then,it summarizes the present practical dilemma from the aspects of risk attributes,technical standards,regional economic differences,and participants’awareness and ability.In the future,academia and practitioners need to focus on public health big data from exploring theoretical paradigms,building mutual trust relationships,optimizing surveillance mechanisms,exploring evidence-based decision-making,and integrating surveillance systems,so as to jointly promote the development of the new practice mode of public health risk surveillance driven by big data.At a time when the global epidemic prevention and control situation is still severe,the theoretical framework and practical reflection provided will help the academic community to form a clearer empirical understanding of big data-driven public health risk surveillance.