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人工智能对气候变化与动物源性疾病的协同整合:实现可持续解决方案的整体方法

Synergistic integration of climate change and zoonotic diseases by artificial intelligence: a holistic approach for sustainable solutions.

作者信息

Bergquist Robert, Zheng Jin-Xin, Zhou Xiao-Nong

机构信息

Geospatial Health, Ingerod, Brastad, Sweden.

School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Sci One Health. 2024 May 21;3:100070. doi: 10.1016/j.soh.2024.100070. eCollection 2024.

Abstract

Artificial intelligence (AI) is a rapidly evolving field that can impel research in communicable diseases with respect to climate projections, ecological indicators and environmental impact, at the same time revealing new, previously overlooked events. A number of zoonotic and vector-borne diseases already show signs of expanding their northern geographical ranges and appropriate risk assessment and decision support are urgently needed. The deployment of AI-enabled monitoring systems tracking animal populations and environmental changes is of immense potential in the study of transmission under different climate scenarios. In addition, AI's capability to identify new treatments should not only accelerate drug and vaccine discovery but also help predicting their effectiveness, while its contribution to genetic pathogen speciation would assist the evaluation of spillover risks with regard to viral infections from animals to human. Close collaboration between AI experts, epidemiologists and other stakeholders is not only crucial for responding to challenges interconnected with a variety of variables effectively, but also necessary to warrant responsible AI use. Despite its wider successful implementation in many fields, AI should be seen as a complement to, rather than a replacement of, traditional public health measures.

摘要

人工智能(AI)是一个快速发展的领域,它能够推动传染病研究在气候预测、生态指标和环境影响方面的进展,同时揭示新的、以前被忽视的事件。一些人畜共患病和媒介传播疾病已经显示出向北扩大地理范围的迹象,因此迫切需要进行适当的风险评估和决策支持。部署能够跟踪动物种群和环境变化的人工智能监测系统在不同气候情景下的传播研究中具有巨大潜力。此外,人工智能识别新治疗方法的能力不仅应加速药物和疫苗的发现,还应有助于预测其有效性,而其对遗传病原体物种形成的贡献将有助于评估动物向人类传播病毒感染的溢出风险。人工智能专家、流行病学家和其他利益相关者之间的密切合作不仅对于有效应对与各种变量相互关联的挑战至关重要,而且对于确保负责任地使用人工智能也是必要的。尽管人工智能在许多领域得到了更广泛的成功应用,但它应被视为传统公共卫生措施的补充,而不是替代。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72f1/11262265/4be411f5e3c6/gr1.jpg

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