Objective  To address the problems of single source of data, untimely warning, uncoordinated response, and difficult decision-making in the traditional single-sectoral surveillance and warning mode for campus infectious diseases in Xiamen, Fujian, and establish a multi-sectoral collaborative campus infectious disease surveillance, early warning and response platform based on the framework of “1 platform + 2 departments + 3 dimensions”.

Methods  From the aspects of surveillance performance and requirements, system design and architecture, the design and application of the Xiamen campus infectious disease surveillance, early warning, and response platform was expounded. The application effects of the platform were analyzed.

Results  Baaed on federated computation, transmission encryption and other technologies, we break down information silos, and achieve safety sharing and fusion of high-sensitiv data from multiple sources in health sector and education sector. A multi-dimensional campus infectious disease surveillance and early warning model of “confirmed cases + syndromes + same space + incubation period” was esliblished, the warning rules were dynamically configured, and the warning signals were divided into four levels of blue, yellow, orange, and red for the automatic signal upgrade and real-time signal release. Through data empowerment, the response to campus epidemic bud-events was optimized, a prevention and control system involving family, school, medical institution and disease prevention institution was established, eventually forming a closed loop of collaborative response. The epidemic curve and spatio-temporal interactive map were used to visualize the levels and the changes of surveillance indicators for campus infectious diseases for the dynamic understanding of epidemic situation to support decision-making.

Conclusion  The establishment of the platform improves the timeliness, sensitivity and accuracy of the surveillance in campus compared with the traditional infectious disease surveillance mode, and realizes multi-dimensional, automatic, and hierarchical early warning and efficient collaborative response across sectors and levels.