Non-traditional data in pandemic preparedness and response: identifying and addressing first and last-mile challenges

• URL: http://arxiv.org/abs/2510.09145v1
• Date: Fri, 10 Oct 2025 08:47:38 GMT
• Title: Non-traditional data in pandemic preparedness and response: identifying and addressing first and last-mile challenges
• Authors: Mattia Mazzoli, Irma Varela-Lasheras, Sonia Namorado, Constantino Pereira Caetano, Andreia Leite, Lisa Hermans, Niel Hens, Polen Türkmen, Kyriaki Kalimeri, Leo Ferres, Ciro Cattuto, Daniela Paolotti, Stefaan Verhulst,
• Abstract summary: The pandemic served as an important test case of complementing traditional public health data with non-traditional data.<n>Around 66% of datasets suffered access problem, with data sharing reluctance for NTD being double that of traditional data.<n>The article can be used to design a roadmap for using NTD to confront a broader array of public health emergencies.
• Score: 1.4825361869504283
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025 [70.29305328364755]
  The International Competition on Human Identification at a Distance (HID) has been organized annually since 2020.<n>The best-performing method reached 94.2% accuracy, setting a new benchmark on this dataset.<n>We analyze key technical trends and outline potential directions for future research in gait recognition.
  arXiv  Detail & Related papers  (2026-02-07T14:22:17Z)
• Medical Imaging AI Competitions Lack Fairness [50.895929923643905]
  We assess fairness along two complementary dimensions: whether challenge datasets are representative of real-world clinical diversity, and whether they are accessible and legally reusable in line with the FAIR principles.<n>Our findings show substantial biases in dataset composition, including geographic location, modality, and problem type-related biases, indicating that current benchmarks do not adequately reflect real-world clinical diversity.<n>These shortcomings expose foundational limitations in our benchmarking ecosystem and highlight a disconnect between leaderboard success and clinical relevance.
  arXiv  Detail & Related papers  (2025-12-19T13:48:10Z)
• Network Intrusion Datasets: A Survey, Limitations, and Recommendations [0.0]
  Data-driven cyberthreat detection has become a crucial defense technique in modern cybersecurity.<n>Despite the importance of data, its scarcity has long been recognized as a major obstacle in NIDS research.
  arXiv  Detail & Related papers  (2025-02-10T17:14:37Z)
• Navigating the Data Trading Crossroads: An Interdisciplinary Survey [33.64953318642493]
  Data has been increasingly recognized as a critical factor in the future economy. However, constructing an efficient data trading market faces challenges such as privacy breaches, data monopolies, and misuse. This paper aims to identify existing problems, research gaps, and propose potential solutions.
  arXiv  Detail & Related papers  (2024-07-16T08:07:16Z)
• Data Acquisition: A New Frontier in Data-centric AI [65.90972015426274]
  We first present an investigation of current data marketplaces, revealing lack of platforms offering detailed information about datasets. We then introduce the DAM challenge, a benchmark to model the interaction between the data providers and acquirers. Our evaluation of the submitted strategies underlines the need for effective data acquisition strategies in Machine Learning.
  arXiv  Detail & Related papers  (2023-11-22T22:15:17Z)
• Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results [73.98594459933008]
  Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets. We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
  arXiv  Detail & Related papers  (2023-04-12T10:29:42Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies [67.34625604583208]
  Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) are considered to engage in Sovereign Data Exchange (SDE) PETs and AETs are technically complex, which impedes their adoption. This study empirically constructs a challenge-oriented technology mapping.
  arXiv  Detail & Related papers  (2022-06-20T08:16:42Z)
• COVID-19: An exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic [3.192308005611312]
  In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide. As of late 2023, over millions have died as a result of COVID-19. Data professionals working with pandemic-relevant data often face significant systemic barriers to accessing, sharing or re-using this data.
  arXiv  Detail & Related papers  (2022-05-24T14:25:09Z)
• On the Use of Data from Multiple Mobile Network Operators in Europe to fight COVID-19 [1.3162012586770573]
  The rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information. This paper presents lessons learnt and results of a unique Business-to-Government (B2G) initiative between several Mobile Network Operators in Europe and the European Commission.
  arXiv  Detail & Related papers  (2021-06-10T10:39:21Z)
• COVID-19 Digital Contact Tracing Applications and Techniques: A Review Post Initial Deployments [2.05040847923906]
  coronavirus disease 2019 (COVID-19) is a severe global pandemic that has claimed millions of lives and continues to overwhelm public health systems. To increase the effectiveness of contact tracing, countries across the globe are leveraging advancements in mobile technology and Internet of Things.
  arXiv  Detail & Related papers  (2021-02-25T10:18:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.