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

2026-04-29Artigo científico Acesso aberto
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datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorMazzoli, Mattia
dc.contributor.authorVarela-Lasheras, Irma
dc.contributor.authorNamorado, Sónia
dc.contributor.authorCaetano, Constantino
dc.contributor.authorLeite, Andreia
dc.contributor.authorHermans, Lisa
dc.contributor.authorHens, Niel
dc.contributor.authorTürkmen, Polen
dc.contributor.authorKalimeri, Kyriaki
dc.contributor.authorFerres,Leo
dc.contributor.authorCattuto, Ciro
dc.contributor.authorPaolotti, Daniela
dc.contributor.authorVerhulst, Stefaan
dc.date.accessioned2026-10-02T10:47:38Z
dc.date.available2026-10-02T10:47:38Z
dc.date.issued2026-04-29
dc.description.abstractThe COVID-19 pandemic served as an important test case of complementing traditional public health data with nontraditional data, such as mobility traces, social media activity, and wearable data, to inform real-time decision-making. Drawing on an expert workshop and a targeted survey of epidemic modelers in Europe, this study assesses the promise and the 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). 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 nontraditional data into epidemic modeling and policymaking. The accompanying survey, gathering experiences from 29 modelers, offers empirical evidence of the barriers faced by modelers during the COVID-19 pandemic and highlights areas where key data were unavailable or underused. The experiences collected through the survey and workshop resulted in ten key actions and three overarching recommendations for public entities, data providers, and stakeholders. 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 had at least one access problem, with data sharing reluctance for nontraditional sources being double that of traditional data (30% vs 15%). Only 10% of respondents reported that they could use all the data they needed. These limitations included issues related to timeliness and granularity of data, as well as 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 laboratories, and networks of scientific ambassadors to bridge the gap between analysis and action. We argue that realizing the full value of nontraditional 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 the COVID-19 pandemic, the study can be used to design a roadmap for using nontraditional data to confront a broader array of public health emergencies, from climate shocks to humanitarian crises.eng
dc.description.sponsorshipThis project was supported by the ESCAPE project (101095619), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. MM, PT, LF, KK, CC, DP, and SV acknowledge support from the Lagrange Project of the ISI Foundation, funded by Fondazione CRT. LF acknowledges support from the Fondo de Investigación y Desarrollo en Salud, Fonis, Project SA24I0124.
dc.identifier.citationJ Med Internet Res. 2026 Apr 29:28:e85540. doi: 10.2196/85540
dc.identifier.doi10.2196/85540
dc.identifier.eissn1438-8871
dc.identifier.issn1439-4456
dc.identifier.pmid42054597
dc.identifier.urihttp://hdl.handle.net/10400.18/11371
dc.language.isoeng
dc.peerreviewedyes
dc.publisherJMIR Publications
dc.relationEfficient and rapidly SCAlable EU-wide evidence-driven Pandemic response plans through dynamic Epidemic data assimilation
dc.relation.hasversionhttps://www.jmir.org/2026/1/e85540
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNontraditional Data
dc.subjectPandemic Preparedness
dc.subjectPandemic Response
dc.subjectData Science
dc.subjectEpidemic Modeling
dc.subjectEpidemiology
dc.subjectEurope
dc.subjectPublic Health
dc.subjectPolíticas de Saúde
dc.titleNontraditional Data in Pandemic Preparedness and Response: Identifying and Addressing First- and Last- Mile Challengeseng
dc.typejournal article
dcterms.referenceshttps://jmir.org/api/download?alt_name=jmir_v28i1e85540_app1.pdf&filename=dfba4a63-4401-11f1-9ee5-c590cf7d8ff7.pdf
dspace.entity.typePublication
oaire.awardNumber101095619
oaire.awardTitleEfficient and rapidly SCAlable EU-wide evidence-driven Pandemic response plans through dynamic Epidemic data assimilation
oaire.awardURIhttp://hdl.handle.net/10400.18/10746
oaire.citation.startPagee85540
oaire.citation.titleJournal of Medical Internet Research
oaire.citation.volume28
oaire.fundingStreamHORIZON Research and Innovation Actions
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameNamorado
person.familyNameCaetano
person.familyNameLeite
person.givenNameSónia
person.givenNameConstantino
person.givenNameAndreia
person.identifier1052436
person.identifier.ciencia-id5415-90E4-3D0E
person.identifier.ciencia-id2F10-F9A9-E8A7
person.identifier.orcid0000-0002-7500-8811
person.identifier.orcid0000-0002-6569-7772
person.identifier.orcid0000-0003-0843-0630
person.identifier.ridD-7168-2015
person.identifier.scopus-author-id14047182500
person.identifier.scopus-author-id57109931300
relation.isAuthorOfPublication4ed6a689-2426-46ea-90e6-1950652a288b
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