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Projeto de investigação
Efficient and rapidly SCAlable EU-wide evidence-driven Pandemic response plans through dynamic Epidemic data assimilation
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Assessing the role of children in the COVID-19 pandemic in Belgium using perturbation analysis
Publication . Angeli, Leonardo; Caetano, Constantino Pereira; Franco, Nicolas; Coletti, Pietro; Faes, Christel; Molenberghs, Geert; Beutels, Philippe; Abrams, Steven; Willem, Lander; Hens, Niel
Understanding the evolving role of different age groups in virus transmission is essential for effective pandemic management. We investigated SARS-CoV-2 transmission in Belgium from November 2020 to February 2022, focusing on age-specific patterns. Using a next generation matrix approach integrating social contact data and simulating population susceptibility evolution, we performed a longitudinal perturbation analysis of the effective reproduction number to unravel age-specific transmission dynamics. From November to December 2020, adults in the [18, 60) age group were the main transmission drivers, while children contributed marginally. This pattern shifted between January and March 2021, when in-person education resumed, and the Alpha variant emerged: children aged under 12 years old were crucial in transmission. Stringent social distancing measures in March 2021 helped diminish the noticeable contribution of the [18, 30) age group. By June 2021, as the Delta variant became the predominant strain, adults aged [18, 40) years emerged as main contributors to transmission, with a resurgence in children’s contribution during September-October 2021. This study highlights the effectiveness of our methodology in identifying age-specific transmission patterns.
Nontraditional Data in Pandemic Preparedness and Response: Identifying and Addressing First- and Last- Mile Challenges
Publication . Mazzoli, Mattia; Varela-Lasheras, Irma; Namorado, Sónia; Caetano, Constantino; Leite, Andreia; Hermans, Lisa; Hens, Niel; Türkmen, Polen; Kalimeri, Kyriaki; Ferres,Leo; Cattuto, Ciro; Paolotti, Daniela; Verhulst, Stefaan
The 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.
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Entidade financiadora
European Commission
European Commission
European Commission
Programa de financiamento
HORIZON Research and Innovation Actions
HORIZON-RIA\HORIZON Action Grant Budget-Based
HORIZON-RIA\HORIZON Action Grant Budget-Based
Número da atribuição
101095619