Completed Public Health & Healthcare Computing & AI

The Epiverse - Distributed Pandemic Tools Program

In plain English

AI plain-English summary

During the COVID-19 pandemic, public health officials could not quickly combine hospital records, travel data, and private-sector mobility logs to track the virus—because pooling that data was legally risky, slow, and expensive. This project builds the software and legal frameworks to let researchers analyse sensitive health and commercial data without ever moving or sharing the raw information. Instead of centralising data in one place, the tools will run analyses across multiple secure locations, returning only aggregate results. The first phase funds open-source epidemiological software, privacy-preserving methods for commercially held data, and a challenge to major cloud providers to design the technical architecture for a second phase. If successful, the programme will create a globally adoptable infrastructure for real-time pandemic modelling using data that currently sits behind legal or commercial barriers—such as airline passenger flows, retail footfall, or mobile phone mobility patterns. This is not fundamental science; it is applied engineering of data-sharing systems. The impact would be faster, legally safer outbreak detection and response, without requiring countries or companies to surrender control of their data.

View original technical description
Covid-19 exposed major gaps in our ability to aggregate and use data for pandemic prevention, detection, and response. Current approaches require pooled data, which is costly, time-consuming, and legally challenging. Distributed and privacy-preserving methods of analysis are an alternative for generating insight and present new opportunities to use commercially held “health-adjacent” data critical for pandemic analysis and modelling. However, despite the appeal of distributed and privacy-preserving methods of analysis, there are few working examples focused on disease analysis and none that are globally adopted. This proposal is to build, deploy, and scale innovative solutions – including infrastructure, tools, and analytical techniques to unlock data and enable distributed analysis. Phase 1 will focus on developing the novel software and privacy-preserving methods that will be deployed in Phase 2: - Funding top teams to develop a suite of generalizable, open-source epidemiological software and tools - Challenge funding call to develop privacy-preserving approaches for deployment on commercially sensitive/privately held data, which could be scaled and deployed in Phase 2. - 200 Days Architecture Challenge aimed at Big Cloud Providers to design the technical architecture for Phase 2. data.org will provide the central convening and coordination role, including grant-making, convening interdisciplinary specialists, project management, and strategic communication.

View the original record at the funder ↗

Researchers

Danil Mikhailov (EPMC Awardee)

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Original classification

Discretionary Award – DSH

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.