Common Data Architecture for COVID-19 & Beyond
Written by Doug Hamrick and Sarah Shuhaibar
Written by Doug Hamrick and Sarah Shuhaibar
Without a common data architecture during the COVID-19 pandemic, federal agencies have faced unprecedented challenges. Decision making and evaluation during all stages of the pandemic lifecycle require specific, reliable, and timely data not only about infections, but also about human behavior and global supply chain networks.
A common architecture is needed to collect, share, and store data. Without the ability to collect and curate a wide variety of data from around the world, agencies have had to rely on data that is maintained in numerous individual systems and formats, making it difficult to aggregate and analyze information.
Federal agencies should create a common data architecture to consolidate existing data and collect new information relevant to multiple dimensions of the COVID-19 pandemic. The architecture should include data about testing, tracing, surveillance, personal protective equipment (PPE), supply chains, hospital bed management, and vaccinations, to name a few. It would also help health services agencies and communities be more prepared before the next pandemic or other public health threats.
This common architecture would streamline the use of artificial intelligence and data science tools. It would also enable development of customized applications to rapidly simulate situations across all stages of the next pandemic. Such common architectures are similar to those used on mobile phones, where a company sets requirements that enable developers to rapidly construct an algorithm or app that is interoperable on multiple devices.
Where should federal agencies begin the process of building a common data architecture? It starts with stakeholders communicating their needs, developing common data definitions, and identifying ownership for information collection and management. The results of this effort will improve coordination and information exchange between national and regional initiatives, and if there is coordination across governments, at the global level.
During the ongoing pandemic response, achieving this level of efficiency is easier said than done. However, if properly prioritized and supported by appropriate resources, it is achievable using state-of-the-art data science methods. As we look to capture lessons learned to prepare for the next pandemic and other public health crises, building a common data architecture should be high on the priority list.
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