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已知和未知的跨界传染病作为混合威胁。

Known and Unknown Transboundary Infectious Diseases as Hybrid Threats.

机构信息

Orion Integrated Biosciences Inc., Manhattan, KS, United States.

出版信息

Front Public Health. 2021 Jul 14;9:668062. doi: 10.3389/fpubh.2021.668062. eCollection 2021.

Abstract

The pathogenicity, transmissibility, environmental stability, and potential for genetic manipulation make microbes hybrid threats that could blur the distinction between peace and war. These agents can fall below the detection, attribution, and response capabilities of a nation and seriously affect their health, trade, and security. A framework that could enhance horizon scanning regarding the potential risk of microbes used as hybrid threats requires not only accurately discriminating known and unknown pathogens but building novel scenarios to deploy mitigation strategies. This demands the transition of analyst-based biosurveillance tracking a narrow set of pathogens toward an autonomous biosurveillance enterprise capable of processing vast data streams beyond human cognitive capabilities. Autonomous surveillance systems must gather, integrate, analyze, and visualize billions of data points from different and unrelated sources. Machine learning and artificial intelligence algorithms can contextualize capability information for different stakeholders at different levels of resolution: strategic and tactical. This document provides a discussion of the use of microorganisms as hybrid threats and considerations to quantitatively estimate their risk to ensure societal awareness, preparedness, mitigation, and resilience.

摘要

微生物的致病性、传染性、环境稳定性和遗传操纵潜力使其成为混合威胁,可能模糊和平与战争之间的界限。这些生物制剂可能低于一个国家的检测、归因和应对能力,并严重影响其健康、贸易和安全。一个能够增强对用作混合威胁的微生物的潜在风险进行地平线扫描的框架,不仅需要准确区分已知和未知的病原体,还需要构建新的场景来部署缓解策略。这要求分析师主导的生物监测从跟踪一组狭窄的病原体转变为能够处理超出人类认知能力的海量数据流的自主生物监测企业。自主监测系统必须从不同的、不相关的来源收集、整合、分析和可视化数十亿个数据点。机器学习和人工智能算法可以为不同利益相关者在不同分辨率的战略和战术层面上提供能力信息的背景。本文讨论了将微生物用作混合威胁的用途,并考虑了对其风险进行定量评估以确保社会意识、准备、缓解和恢复的问题。

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