Logo do repositório
 
Publicação

Nontraditional data in pandemic preparedness and response:

2026Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorMazzoli, Mattia
dc.contributor.authorVarela-Lasheras, Irma
dc.contributor.authorNamorado, Sónia
dc.contributor.authorCaetano, Constantino Pereira
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.contributor.institutionLaboratório Associado de Translacção e Inovação para a Saúde Global - LA Real (Pólo ENSP)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - Pólo ENSP
dc.contributor.institutionCentro de Investigação em Saúde Pública (CISP/PHRC)
dc.contributor.institutionEscola Nacional de Saúde Pública (ENSP)
dc.contributor.pblJMIR Publications
dc.date.accessioned2026-07-16T09:56:02Z
dc.date.available2026-07-16T09:56:02Z
dc.date.issued2026
dc.descriptionPublisher Copyright: © Mattia Mazzoli, Irma Varela-Lasheras, Sónia Namorado, Constantino Pereira Caetano, Andreia Leite, Lisa Hermans, Niel Hens, Polen Türkmen, Kyriaki Kalimeri, Leo Ferres, Ciro Cattuto, Daniela Paolotti, Stefaan Verhulst.
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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent832897
dc.identifier.doi10.2196/85540
dc.identifier.issn1439-4456
dc.identifier.otherPURE: 168417961
dc.identifier.otherPURE UUID: 3ff1fff0-9f44-45fe-98a9-acb82d435b8e
dc.identifier.otherScopus: 105037475407
dc.identifier.otherPubMed: 42054597
dc.identifier.otherPubMedCentral: PMC13128049
dc.identifier.otherWOS: 001757955100001
dc.identifier.otherORCID: /0000-0003-0843-0630/work/220954388
dc.identifier.urihttp://hdl.handle.net/10362/204567
dc.identifier.urlhttps://www.scopus.com/pages/publications/105037475407
dc.language.isoeng
dc.peerreviewedyes
dc.subjectdata science
dc.subjectepidemic modeling
dc.subjectnontraditional data
dc.subjectpandemic preparedness
dc.subjectpandemic response
dc.subjectHealth Informatics
dc.subjectSDG 3 - Good Health and Well-being
dc.subjectSDG 13 - Climate Action
dc.titleNontraditional data in pandemic preparedness and response:en
dc.title.subtitleidentifying and addressing first- and last-mile challengesen
dc.typejournal article
degois.publication.titleJournal of Medical Internet Research
degois.publication.volume28
dspace.entity.typePublication
rcaap.rightsopenAccess

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Mazzoli_2026_J_Med_Internet_Res_28_e85540.pdf
Tamanho:
813.38 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

Home collection (ENSP)