With the acceleration of globalization and urbanization, public health events such as emerging infectious diseases have occurred frequently, posing serious threats to human health and social stability. Traditional public health monitoring and early warning systems have shown clear limitations in terms of the breadth, depth, timeliness of data acquisition, as well as the integration and analysis of multi-source heterogeneous data. Leveraging its characteristics of massive volume, high velocity, and diversity, big data technology provides critical technical support for building a new generation of intelligent public health risk early warning mechanisms. This paper systematically explores such mechanisms by first analyzing the dilemmas faced by traditional early warning systems, and then elaborating on the core logic, technical architecture, and data ecosystem of big data–empowered public health early warning. Innovatively, energy and chemical enterprises are introduced as a representative case to demonstrate how internal early warning systems constructed through big data can, in turn, enhance regional public safety.
Bai Cheng-De (Wed,) studied this question.