Global Real-Time Surveillance of Emerging Antimicrobial Resistance Using Multi-Source Data Analytics

Abstract

Antimicrobial resistance (AMR) is a worldwide problem and a deadly menace to human health, requiring early diagnosis and treatment. The conventional surveillance system can be characterized by slow reporting, inability to integrate heterogeneous data, and thus effective investigation of the provided response. This paper suggests a real-time, multi-source, and global surveillance framework of emerging AMR over a real-time environment. The screening of genomic sequencing information, environmental surveillance, movement, and open-source data on social networks and news channels allow identifying the trends of resistance and hotspots near real-time by integrating clinical laboratory reports, DNA analysis, and environmental surveillance along with population movement. Innovative machine learning and geospatial analytics are used to recognize patterns and anticipate new resistance and proactive interventions. Pilot applications prove that the framework can offer actionable information to policymakers, health care providers, and health authorities in the world. This strategy emphasizes the potential of transforming data-driven surveillance in the prevention of the AMR proliferation and enhancement of global health security.