Title: Integrating big data collaboration models: advancements in health security and infectious disease early warning systems

Authors: Jiexuan Cui; Ye Deng; Qian Hao

Addresses: Department of Economic and Trade, Hebei Vocational University of Industry and Technology, Shijiazhuang, Hebei, 050000, China ' Satellite TV Channel, Hebei Radio and TV Station, Shijiazhuang, Hebei, 050000, China ' Department of Economic and Trade, Hebei Vocational University of Industry and Technology, Shijiazhuang, Hebei, 050000, China

Abstract: In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

Keywords: big and thick data analytics; blockchain; public health; early warning model; collaborative model.

DOI: 10.1504/IJDMB.2024.139486

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.3/4, pp.471 - 491

Received: 31 Aug 2023
Accepted: 26 Oct 2023
Published online: 02 Jul 2024 *

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