Unit of Veterinary Public Health and Epidemiology, University of Veterinary Medicine Vienna, Veterinaerplatz 1, 1210, Vienna, Austria.
Complexity Science Hub Vienna, Josefstaedter Strasse 39, 1080, Vienna, Austria.
Sci Data. 2022 Jul 23;9(1):438. doi: 10.1038/s41597-022-01543-8.
The zoonotic origin of SARS-CoV-2, the etiological agent of COVID-19, is not yet fully resolved. Although natural infections in animals are reported in a wide range of species, large knowledge and data gaps remain regarding SARS-CoV-2 in animal hosts. We used two major health databases to extract unstructured data and generated a global dataset of SARS-CoV-2 events in animals. The dataset presents harmonized host names, integrates relevant epidemiological and clinical data on each event, and is readily usable for analytical purposes. We also share the code for technical and visual validation of the data and created a user-friendly dashboard for data exploration. Data on SARS-CoV-2 occurrence in animals is critical to adapting monitoring strategies, preventing the formation of animal reservoirs, and tailoring future human and animal vaccination programs. The FAIRness and analytical flexibility of the data will support research efforts on SARS-CoV-2 at the human-animal-environment interface. We intend to update this dataset weekly for at least one year and, through collaborations, to develop it further and expand its use.
SARS-CoV-2 即 COVID-19 的病原体,其动物源性尚未完全明确。尽管报告了多种动物物种中的自然感染,但关于动物宿主中的 SARS-CoV-2 仍存在大量知识和数据空白。我们使用两个主要的健康数据库提取非结构化数据,并生成了一个 SARS-CoV-2 在动物中发生事件的全球数据集。该数据集提供了经过协调的宿主名称,整合了每个事件的相关流行病学和临床数据,并且易于用于分析目的。我们还分享了用于数据技术和可视化验证的代码,并创建了一个用户友好的数据探索仪表板。关于动物中 SARS-CoV-2 发生情况的数据对于调整监测策略、防止动物储存库形成以及调整未来人类和动物疫苗接种计划至关重要。该数据的 FAIRness 和分析灵活性将支持人类-动物-环境界面上 SARS-CoV-2 的研究工作。我们打算至少在一年内每周更新此数据集,并通过合作进一步开发和扩大其使用。