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Nontraditional Data in Pandemic Preparedness and Response: Identifying and Addressing First- and Last-Mile

Mattia Mazzoli1, Irma Varela-Lasheras2, Sónia Namorado2,3

  • 1ISI Foundation, Via Della Rocca 20, Turin, 10123, Italy, 39 011 6603090.

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Summary
This summary is machine-generated.

Nontraditional data, like mobility and social media, showed promise during COVID-19 but faced access and integration challenges. Overcoming these requires better data infrastructure and cross-sector collaboration for future public health emergencies.

Keywords:
data scienceepidemic modelingnontraditional datapandemic preparednesspandemic response

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Area of Science:

  • Public Health Informatics
  • Epidemiology
  • Data Science

Background:

  • The COVID-19 pandemic highlighted the need to integrate nontraditional data sources (e.g., mobility, social media, wearables) with traditional public health data for real-time decision-making.
  • Existing research often focuses on the potential of these data, but less on the practical challenges of their implementation in pandemic response.

Purpose of the Study:

  • To assess the promise and persistent limitations of nontraditional data in pandemic preparedness and response.
  • To identify and categorize challenges in accessing, harmonizing, and utilizing nontraditional data for epidemic modeling and policymaking.
  • To propose actionable strategies and recommendations for improving the integration of nontraditional data into public health decision-making.

Main Methods:

  • An expert workshop in Brussels (March 2024) with 50 participants from public health, data science, and policy sectors.
  • A targeted survey of 29 epidemic modelers in Europe detailing their experiences with nontraditional data during the COVID-19 pandemic.
  • Analysis of "first-mile" (data access/harmonization) and "last-mile" (policy translation) challenges.

Main Results:

  • Significant barriers exist in data access (66% of datasets had issues), quality, and interoperability, particularly for nontraditional data sources.
  • Data sharing reluctance was double for nontraditional data (30%) compared to traditional data (15%).
  • Only 10% of modelers could access all necessary data, citing issues with timeliness, granularity, linkage, comparability, and bias.

Conclusions:

  • Overcoming "first-mile" challenges requires robust technical and legal frameworks for data access, including data inventories and standardization protocols.
  • "Last-mile" challenges necessitate solutions like fusion centers and decision accelerator laboratories to bridge the gap between data analysis and policy action.
  • Realizing the value of nontraditional data demands sustained investment in institutional readiness, cross-sectoral collaboration, and a culture of data solidarity for future public health emergencies.