Integrating patient metadata and pathogen genomic data: advancing pandemic preparedness with a multi-parametric simulator.

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES
Bonjean Maxime, Ambroise Jérôme, Orchard Francisco, Sentis Alexis, Hurel Julie, Hayes Jessica S, Connolly Máire A, Jean-Luc Gala
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引用次数: 0

Abstract

Stakeholder training is essential for handling unexpected crises swiftly, safely, and effectively. Functional and tabletop exercises simulate potential public health crises using complex scenarios with realistic data. These scenarios are designed by integrating datasets that represent populations exposed to a pandemic pathogen, combining pathogen genomic data generated through high-throughput sequencing (HTS) together with patient epidemiological, clinical, and demographic information. However, data sharing between EU member states faces challenges due to disparities in data collection practices, standardisation, legal frameworks, privacy, security regulations, and resource allocation. In the Horizon 2020 PANDEM-2 project, we developed a multi-parametric training tool that links pathogen genomic data and metadata, enabling training managers to enhance datasets and customise scenarios for more accurate simulations. The tool is available as an R package: https://github.com/maous1/Pandem2simulator and as a Shiny application: https://uclouvain-ctma.Shinyapps.io/Multi-parametricSimulator/ , facilitating rapid scenario simulations. A structured training procedure, complete with video tutorials and exercises, was shown to be effective and user-friendly during a training session with twenty PANDEM-2 participants. In conclusion, this tool enhances training for pandemics and public health crises preparedness by integrating complex pathogen genomic data and patient contextual metadata into training simulations. The increased realism of these scenarios significantly improves emergency responder readiness, regardless of the biological incident's nature, whether natural, accidental, or intentional.

整合患者元数据和病原体基因组数据:用多参数模拟器推进大流行防范。
利益相关者培训对于快速、安全和有效地处理意外危机至关重要。功能练习和桌面练习使用具有现实数据的复杂情景模拟潜在的公共卫生危机。这些情景是通过整合代表大流行病原体暴露人群的数据集,将高通量测序(HTS)产生的病原体基因组数据与患者流行病学、临床和人口统计信息结合起来设计的。然而,由于数据收集实践、标准化、法律框架、隐私、安全法规和资源分配方面的差异,欧盟成员国之间的数据共享面临挑战。在“地平线2020”PANDEM-2项目中,我们开发了一个多参数培训工具,将病原体基因组数据和元数据联系起来,使培训管理人员能够增强数据集并定制场景,以实现更准确的模拟。该工具以R包的形式提供:https://github.com/maous1/Pandem2simulator和Shiny应用程序的形式提供:https://uclouvain-ctma.Shinyapps.io/Multi-parametricSimulator/,便于快速场景模拟。在一次有20名第二次全球发展计划参与者参加的培训会议上,显示了一种有组织的训练程序,包括录象教程和练习,是有效和便于使用的。总之,该工具通过将复杂的病原体基因组数据和患者背景元数据整合到训练模拟中,加强了流行病和公共卫生危机防范培训。无论生物事件的性质如何,无论是自然的、意外的还是故意的,这些情景的真实性大大提高了应急响应人员的准备程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Research Notes
BMC Research Notes Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
3.60
自引率
0.00%
发文量
363
审稿时长
15 weeks
期刊介绍: BMC Research Notes publishes scientifically valid research outputs that cannot be considered as full research or methodology articles. We support the research community across all scientific and clinical disciplines by providing an open access forum for sharing data and useful information; this includes, but is not limited to, updates to previous work, additions to established methods, short publications, null results, research proposals and data management plans.
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