JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic served as an important test case of complementing traditional public health data with non-traditional data such as mobility traces, social media activity, and wearables data to inform real-time decision-making.

Objective:

Drawing on an expert workshop and a targeted survey of European modelers, this article assesses the promise and persistent limitations of such data in pandemic preparedness and response. We distinguish between "first-mile" challenges (obstacles to accessing and harmonizing data) and "last-mile" challenges (difficulties in translating insights into actionable policy interventions).

Methods:

The expert workshop convened in March 2024 in Brussels brought together 50 participants including public health professionals, data scientists, policymakers, and industry leaders to reflect on lessons learned and define strategies for better integration of non-traditional data into epidemic modeling and policy making. The accompanying survey, gathering experiences from 29 modelers, offers empirical evidence of the barriers faced by modelers during COVID-19 and highlights areas where key data was unavailable or underutilized.

Results:

Our findings reveal ongoing issues with data access, quality, and interoperability, as well as institutional and cognitive barriers to evidence-based decision-making. Approximately 66% of all datasets suffered at least one access problem, with data sharing reluctance for non-traditional sources being double that of traditional data (30% vs 15%). Only 10% of respondents reported they could use all the data they needed. These limitations included timeliness and granularity of data, issues with linkage, comparability, and biases. To overcome these hurdles, we propose a set of enabling mechanisms, including data inventories, standardization protocols, simulation exercises, data stewardship roles, and data collaboratives. 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.

Conclusions:

We argue that realizing the full value of non-traditional data 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 non-traditional data to confront a broader array of public health emergencies, from climate shocks to humanitarian crises.