Data-driven public health security

Public health security incidents have emerged as one of the most pressing threats to human survival in the 21st century. This is particularly evident with the resurgence and re-emergence of infectious diseases, which pose a grave risk to global public health and socio-economic stability. In light of these challenges, the rapid development of big data analysis technology has paved the way for data-driven public health security, a crucial tool in our arsenal for infectious disease prevention, control, and informed decision-making. Data-driven public health security revolves around the collection, analysis, and utilization of extensive big data resources to prevent and control epidemics, which can significantly enhance our ability to safeguard public health. Among the various categories of public health security big data, genetic data stands out as a pivotal component. It provides valuable insights into microbial pathogen evolution and aids in the judgment of epidemic development from a molecular genetic evolutionary perspective. Research in this field strives to offer a scientific foundation for managing public health security by comprehensively analyzing large-scale genetic data, ultimately empowering us to respond more effectively to diverse public health challenges. Effective compliance management, intelligent analysis, and scientific application of pathogen genetic data are vital for constructing a robust framework for public health security. Firstly, in the context of current global outbreaks, pathogen genetic data has become a cornerstone for epidemic prevention and control. The urgent need to manage these data in compliance with standards is paramount. Secondly, intelligent analysis technologies play a pivotal role in extracting hidden information from pathogen genetic-related data, providing precise guidance for infectious disease prevention and control. Through the integration and analysis of pathogen genetic data, we can track pathogen evolution, predict new variants and transmission paths, issue timely warnings about transmission risks, and formulate effective prevention and control strategies. Finally, scientific application bridges the gap between theoretical knowledge and practical operations. Integrating pathogen genetic-related data with real-world scenarios allows us to develop more effective prevention and control measures, harness the potential of genetic data in public health security management, and elevate overall public health and security. This paper delves into the role of the China National Science Data Centers in the establishment of a comprehensive "storage-management-use" framework for genetic data in the realm of public health security. The objective is to enhance public awareness and understanding of genetic data-driven public health security. The China National Science Data Centers undertake the responsibility of collecting, integrating, and storing biomedical big data nationwide, facilitating big data mining applications. Their services encompass data exchange, integration and sharing, monitoring, and early warning platforms. These platforms ensure the timely, high-quality, and reliable availability of pathogenic microbial data and contribute to effective emergency responses, data usability improvement, spatiotemporal pathogenic genomic data tracking, and high-risk mutation and variant prediction using artificial intelligence. Taking inspiration from the experience of dealing with COVID-19, the databases and service platforms provided by the China National Genomics Data Center and China National Microbiology Data Center have played a pivotal role in real-time monitoring and the effective prevention and control of the pandemic. Furthermore, these resources serve as crucial technical reserves for addressing future emerging infectious public health crises. The development of these technologies and platforms has substantially bolstered China's capabilities in data-driven public health security, elevating its influence in global public health governance.