Tuesday, January 21, 2020
News on Pathogens and Preparedness

Artificial Intelligence and Big Data Analytics for Biodefence: Implications for Threat Assessment And Biosurveillance

The use of AI and big data analytics for the purpose of intelligence is a new discipline of information extraction that changes every stage of intelligence work, from acquisition to processing to formulating the intelligence picture and translating the information into operational steps.

With regard to biological hazards, threat assessment involves obtaining timely, accurate, and relevant intelligence related to the malevolent use of biological agents, and the recognition of existing and future trends and patterns in the evolving threat of biological weapons. These activities encompass the collection and analysis of a multitude of information about potential adversaries and their interest and capabilities in the areas of biology and biotechnology, in order to enable intelligence collection analysts to anticipate the challenges and support the development of medical and non-medical countermeasures. Artificial intelligence and big data analytics are well suited to support these efforts.

Biosurveillance systems collect and analyse vast quantities of diverse real-time data from many sources to provide governments with advance warning of disease outbreak or bioweapons attack. The capacity to anticipate and monitor when and where an outbreak may occur and how a pathogen may be transmitted has the potential to substantially improve response strategies.

In addition, government institutions in the US and Europe have begun funding research and development that uses machine learning to identify whether a DNA sequence is encoding part of a virulent pathogen, and researchers are beginning to make progress in developing AI-based screening tools. Among them is the US Intelligence Advanced Research Projects Agency (IARPA), who has launched an initiative – the Functional Genomic and Computational Assessment of Threats (Fun GCAT) program, to design better algorithms for spotting potentially threatening sequences.

Read the more by Roman Kernchen at the Eyvor Institute

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