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动物疾病全球负担信息学战略、数据质量和模型互操作性。

Global Burden of Animal Diseases informatics strategy, data quality and model interoperability.

出版信息

Rev Sci Tech. 2024 Aug;43:96-107. doi: 10.20506/rst.43.3522.

DOI:10.20506/rst.43.3522
PMID:39222107
Abstract

The estimation of the global burden of animal diseases requires the integration of multidisciplinary models: economic, statistical, mathematical and conceptual. The output of one model often serves as input for another; therefore, consistency of the model components is critical. The Global Burden of Animal Diseases (GBADs) Informatics team aims to strengthen the scientific foundations of modelling by creating tools that address challenges related to reproducibility, as well as model, data and metadata interoperability. Aligning with these aims, several tools are under development: a) GBADs'Trusted Animal Information Portal (TAIL) is a data acquisition platform that enhances the discoverability of data and literature and improves the user experience of acquiring data. TAIL leverages advanced semantic enrichment techniques (natural language processing and ontologies) and graph databases to provide users with a comprehensive repository of livestock data and literature resources. b) The interoperability of GBADs'models is being improved through the development of an R-based modelling package and standardisation of parameter formats. This initiative aims to foster reproducibility, facilitate data sharing and enable seamless collaboration among stakeholders. c) The GBADs Knowledge Engine is being built to foster an inclusive and dynamic user community by offering data in multiple formats and providing user-friendly mechanisms to garner feedback from the community. These initiatives are critical in addressing complex challenges in animal health and underscore the importance of combining scientific rigour with user-friendly interfaces to empower global efforts in safeguarding animal populations and public health.

摘要

全球动物疾病负担的估计需要整合多学科模型

经济、统计、数学和概念。一个模型的输出往往是另一个模型的输入;因此,模型组件的一致性至关重要。全球动物疾病负担(GBADs)信息学团队旨在通过创建解决与可重复性以及模型、数据和元数据互操作性相关挑战的工具,来加强建模的科学基础。为了实现这些目标,正在开发几种工具:a)GBADs 的可信动物信息门户(TAIL)是一个数据采集平台,可提高数据和文献的可发现性,并改善用户获取数据的体验。TAIL 利用先进的语义丰富技术(自然语言处理和本体论)和图形数据库,为用户提供牲畜数据和文献资源的综合存储库。b)通过开发基于 R 的建模包和参数格式标准化,正在提高 GBADs 模型的互操作性。这一举措旨在促进可重复性、促进数据共享并实现利益相关者之间的无缝协作。c)正在构建 GBADs 知识引擎,以通过提供多种格式的数据并提供用户友好的机制来从社区中获取反馈,从而促进一个包容和动态的用户社区。这些举措对于应对动物健康方面的复杂挑战至关重要,并强调了将科学严谨性与用户友好的界面相结合,以增强保护动物种群和公共卫生的全球努力的重要性。

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