Improving the Public Health Pandemic Response via an Open, Consensus-Driving Genomic Contextual Data Standard

A key to the SARS-CoV-2 pandemic response has been the sequencing and sharing of large amounts of genomic data and associated contextual information. While genomic data (genetic code) is one important part of the response, it is the contextual data that tells us who was infected, where infection probably occurred, what their clinical outcomes were, and what the sampling methodology was. Different regions have created different standards of contextual information collection, often resulting in data sets that are difficult to merge and compare across administrations. We need to see the bigger picture, yet we cannot create it from incompatible pieces. This proposal is for the creation of a community developed data standard that is adaptive to user needs, accessible, and formatted such that it can be readily processed by a computer. This will be accomplished via engagement with genomic domain experts and data structure scientists. Qualitative semi-structured interviews with domain experts will assist in determining relevant contextual data needs and research questions regarding SARS-CoV-2 transmission scenarios. Information scientists will be consulted to help convert this information into a data model, a means of organizing data elements and the relationships between them, before transforming it into computer code. This work will then be formulated into a protocol that will support other researchers to generate and collaborate on their own data standard scenarios. The end result will be an accessible specification data structure that aids in the creation of compatible genomic contextual data sets and enables computational processing of complex research questions, thereby accelerating public health pandemic response time across disparate health jurisdictions.