人工智能将如何影响流行病应对的未来?来自数据分析的早期线索。

IF 3.3 3区 医学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Risk Analysis Pub Date : 2025-09-10 DOI:10.1111/risa.70103
Benjamin D Trump, Stephanie Galaitsi, Jeff Cegan, Igor Linkov
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引用次数: 0

摘要

2019冠状病毒病大流行暴露了我们在复杂、相互关联的系统中管理系统性风险方面的重大漏洞。本综述探讨了人工智能和数据分析可以显著加强大流行防范、应对和恢复的10个关键领域。所分析的挑战包括早期预警系统不足、资源需求实时数据不足以及传统流行病学模型在捕捉复杂疾病动态方面的局限性。为了解决这些问题,我们探索了人工智能应用的潜力,包括基于机器学习的监测、用于改进流行病学建模的深度学习,以及人工智能驱动的非药物干预优化。这些技术有望更及时、更准确、更细致地分析大流行风险,从而在迅速演变的危机中支持基于证据的决策。然而,在大流行应对中实施人工智能带来了重大的道德和治理挑战,特别是在隐私、公平和问责制方面。我们分析了人工智能在不断发展的应急响应数据分析领域的前景和挑战,并强调了前进的关键步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.

The COVID-19 pandemic has exposed critical gaps in our management of systemic risks within complex, interconnected systems. This review examines 10 key areas where artificial intelligence (AI) and data analytics can significantly enhance pandemic preparedness, response, and recovery. Inadequate early warning systems, insufficient real-time data on resource needs, and the limitations of traditional epidemiological models in capturing complex disease dynamics are among the challenges analyzed. To address these issues, we explore the potential of AI applications, including machine learning-based surveillance, deep learning for improved epidemiological modeling, and AI-driven optimization of non-pharmaceutical interventions. These technologies offer the promise of more timely, accurate, and granular analysis of pandemic risks, thereby supporting evidence-based decision-making in rapidly evolving crises. However, implementing AI in pandemic response raises significant ethical and governance challenges, particularly concerning privacy, fairness, and accountability. We parse the promise and challenges of AI in the evolving space of emergency response data analytics and highlight critical steps forward.

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来源期刊
Risk Analysis
Risk Analysis 数学-数学跨学科应用
CiteScore
7.50
自引率
10.50%
发文量
183
审稿时长
4.2 months
期刊介绍: Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include: • Human health and safety risks • Microbial risks • Engineering • Mathematical modeling • Risk characterization • Risk communication • Risk management and decision-making • Risk perception, acceptability, and ethics • Laws and regulatory policy • Ecological risks.
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