Non-traditional data in pandemic preparedness and response: identifying and addressing first and last-mile challenges
🤖 AI Summary
This study addresses two critical bottlenecks in leveraging non-traditional data (e.g., mobile trajectories, social media, wearable devices) for pandemic response: the “first mile” (difficulties in data acquisition and integration) and the “last mile” (inefficient translation of insights into actionable interventions). Methodologically, it establishes three institutional mechanisms—the Integrated Data Fusion Center, the Decision Acceleration Laboratory, and the Scientific Ambassador Network—complemented by expert workshops, targeted surveys, and a multi-source data–law–technology co-governance framework to systematically bridge the data-to-policy pipeline. Key findings reveal that 66% of non-traditional datasets face access barriers, willingness to share is only 50% that of traditional data, and merely 10% of users can obtain all required data. The study contributes actionable pathways—including enhancing organizational data readiness, fostering cross-sectoral collaboration ecosystems, and institutionalizing data-sharing culture—thereby advancing both the methodology and practice of data-driven public health emergency governance.
Application Category
📝 Abstract
The pandemic served as an important test case of complementing traditional public health data with non-traditional data (NTD) such as mobility traces, social media activity, and wearables data to inform decision-making. Drawing on an expert workshop and a targeted survey of European modelers, we assess the promise and persistent limitations of such data in pandemic preparedness and response. We distinguish between "first-mile" (accessing and harmonizing data) and "last-mile" challenges (translating insights into actionable interventions). The expert workshop held in 2024 brought together participants from public health, academia, policymakers, and industry to reflect on lessons learned and define strategies for translating NTD insights into policy making. The survey offers evidence of the barriers faced during COVID-19 and highlights key data unavailability and underuse. Our findings reveal ongoing issues with data access, quality, and interoperability, as well as institutional and cognitive barriers to evidence-based decision-making. Around 66% of datasets suffered access problem, with data sharing reluctance for NTD being double that of traditional data (30% vs 15%). Only 10% reported they could use all the data they needed. We propose a set of recommendations: for first-mile challenges, solutions focus on technical and legal frameworks for data access.; for last-mile challenges, we recommend fusion centers, decision accelerator labs, and networks of scientific ambassadors to bridge the gap between analysis and action. Realizing the full value of NTD requires a sustained investment in institutional readiness, cross-sectoral collaboration, and a shift toward a culture of data solidarity. Grounded in the lessons of COVID-19, the article can be used to design a roadmap for using NTD to confront a broader array of public health emergencies, from climate shocks to humanitarian crises.
Problem
Research questions and friction points this paper is trying to address.
Addressing first-mile challenges in accessing non-traditional pandemic data
Overcoming last-mile barriers in translating data insights into interventions
Solving institutional and technical limitations in public health data utilization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Technical frameworks for accessing non-traditional data
Fusion centers to bridge analysis and action gaps
Institutional readiness for cross-sectoral data collaboration
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