Project Covid-19-Community crowdsources a Knowledge Graph that links heterogenous data about COVID-19
The Covid-19-Community project will help answer several key questions about the pandemic, such as “What characteristics are in common among outbreaks in the US and other locations?”, “How different are viral strains, and how these differences affect transmission in different regions?”, or “How similar or different the patterns of the coronavirus infection spread and its impacts are compared to previous pandemics?”
The multitude of diverse COVID-19-related data streams, which are rapidly made available online with little coordination or reliance on common standards, creates enormous challenges for researchers trying to answer such questions today, and analyze and predict patterns of the pandemic in its cross-disciplinary complexity. This project will do so by linking diverse information about pathogens, health data, and environmental indicators into a common knowledge graph, to let researchers trace the virus in different geographic conditions and provide input into effective intervention policies.
The results of the collaborative knowledge network development will be published as a continuously updated online dashboard for the general public and a collection of Jupyter Notebooks to help researchers explore various aspects of the knowledge graph and improve our understanding of how the virus is spread and what invention strategies are most effective.
Peter Rose (Structural Bioinformatics Lab),
Ilya Zaslavsky, (Spatial Information Systems Lab)
@ San Diego Supercomputer Center, UC San Diego