SARS-ANI: A Global Open Access Dataset of Reported SARS-CoV-2 Events in Animals
Entry type: Single project
Publishing organisation: Complexity Science Hub Vienna
Organisation size: Small
Publication date: 2022-04-11
Authors: Afra Nerpel; Liuhuaying Yang; Johannes Sorger; Annemarie Käsbohrer; Chris Walzer; Amélie Desvars-Larrive
The Complexity Science Hub Vienna (CSH) is a Vienna-based research organization with the aim to bundle, coordinate and advance the research of complex systems, system analysis and big data science in Austria. In addition to scientific research, we create data visualizations and data stories for science dissemination.
This dashboard is part of “SARS-ANI: A Global Open Access Dataset of Reported SARS-CoV-2 Events in Animals”, a research project led by Amélie Desvars-Larrive from CSH and University of Veterinary Medicine Vienna, Austria.
The SARS-ANI dashboard provides intuitive insights into specific aspects of SARS-CoV-2 events in animals at-a-glance. The visual representations of the data feature multiple selected topics, starting from a general overview and leading to more specific questions, such as variants across species or reported clinical signs. The dashboard intends to support public education about the risk of SARS-CoV-2 transmission between humans and animals and raise public awareness about possible wildlife conservation issues posed by the SARS-CoV-2 pandemic.
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, and created a user-friendly visualization dashboard for data exploration.This dashboard thus facilitates access to the data, favour animal health information sharing, and foster global understanding of the data among the scientific community, stakeholders, and the public.
The continuous analysis of SARS-CoV-2 occurrence data in animals is especially critical to adapting monitoring, surveillance and vaccination programs for animals and humans in a timely manner and evaluating the developing threat SARS-CoV-2 represents for public and animal health as well as biodiversity and conservation.
The data used in this dashboard is manually collected and updated weekly from two major databases: the Program for Monitoring Emerging Diseases ProMED-mail and the World Organisation for Animal Health (OIE) World Animal Health Information System OIE-WAHIS. The collected data is unstructured (narrative text) and/or available in multiple excel sheets or PDF files, therefore not usable without preliminary, time-consuming curation and formatting steps.
The generated dataset is publicly available on our Github(https://github.com/amel-github/sars-ani) and readily usable for analytical purposes. Using harmonized taxonomic names, the SARS-ANI Dataset greatly facilitates access to and re-use of data on SARS-CoV-2 events in animals. We provide a detailed description of the dataset and supplement it with user-friendly documentation and materials (code and archived reports) to enhance data comprehension and use.
The visual elements in the dashboard were custom developed using the D3.js library. All of them are responsive, adapting to the different device sizes including small mobile displays, and are updated automatically when the dataset is updated.
Context about the project:
The dataset we generated is the collaborative work of scientific researchers affiliated to Complexity Science Hub Vienna, Austria, University of Veterinary Medicine Vienna, Austria, and Wildlife Conservation Society, New York, United States. The dashboard is created by researchers from Complexity Science Hub Vienna, Austria.
What can other journalists learn from this project?
This is the first comprehensive global dataset on SARS-CoV-2 events in animals that can be easily imported, processed, and analysed. With the visualization dashboard, the journalists can quickly grasp the main insights from the datasets and find new journalistic stories in both print and digital formats.