全球动物疾病负担信息化战略,数据质量和模型互操作性
Revue scientifique et technique (International Office of Epizootics)
|September 2, 2024
概括
全球动物疾病负担 (GBADs) 信息学团队正在开发工具,以改善数据可访问性,模型互操作性和社区参与,以更好地估计动物疾病负担. 这些信息解决方案提高了全球动物健康倡议的科学严谨性和用户体验.
科学领域:
- 兽医信息学 兽医信息学
- 流行病学 流行病学
- 计算生物学 计算生物学
背景情况:
- 估计全球动物疾病负担需要整合多种多学科模型 (经济,统计,数学,概念).
- 确保模型组件之间的一致性至关重要,因为一个模型的输出通常会进入另一个模型.
- 在可重复性,模型互操作性和数据/元数据管理方面的挑战阻碍了准确的全球负担估计.
研究的目的:
- 加强动物疾病建模的科学基础.
- 开发解决可重复性和互操作性挑战的信息学工具.
- 提高动物卫生数据和文献的可发现性和可访问性.
主要方法:
- 使用语义丰富 (NLP,本体学) 和图形数据库开发可信动物信息门户 (TAIL).
- 创建基于R的建模包,并对参数格式进行标准化,以提高模型的互操作性.
- 构建GBADs知识引擎以促进社区参与并提供多种格式的数据.
主要成果:
- TAIL提高了数据的可查性和用户体验,用于获取畜牧业数据和文献.
- 标准化参数和R套件提高了GBADs模型的互操作性,可重复性和协作性.
- 知识引擎促进社区反和数据可访问性.
结论:
- 开发的信息工具对于应对复杂的动物健康挑战至关重要.
- 将科学严谨性与用户友好的界面相结合,为全球动物健康和公共健康的努力提供了力量.
- 这些举措是强大的全球动物疾病负担估计的基础.
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