Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers’ perspective

作者：Esther van Kleef, Wim Van Bortel, Elena Arsevska, Luca Busani, Simon Dellicour, Laura Di Domenico, Marius Gilbert, Sabine van Elsland, Moritz U. G. Kraemer, Shengjie Lai, Philippe Lemey, Stefano Merler, Zoran Milosavljević, Annapaola Rizzoli, Danijela Simić, Andrew J. Tatem, Maguelonne Teisseire, William Wint, Vittoria Colizza, Chiara Poletto · 发表于：medRxiv · 年份：2025 · DOI：10.1101/2025.03.12.25323819 · 被引用次数：3 · 研究领域：demographic modeling and climate adaptation

Abstract Introduction Advanced outbreak analytics played a key role in governmental decision-making as the COVID-19 pandemic challenged health systems globally. This study assessed the evolution of European modelling practices, data usage, gaps, and interactions between modellers and decision-makers to inform future investments in epidemic-intelligence globally. Methods We conducted a two-stage semi-quantitative survey among modellers in a large European epidemic-intelligence consortium. Responses were analysed descriptively across early, mid-, and late-pandemic phases. Policy citations in Overton were used to assess the policy impact of modelling. Findings Our sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modeling initially prioritised understanding epidemic dynamics, while evaluating non-pharmaceutical interventions and vaccination impacts became equally important in later phases. ‘Traditional’ surveillance data (e.g. case linelists) were widely used in near-real time, while real-time non-traditional data (notably social contact and behavioural surveys), and serological data were frequently reported as lacking. Data limitations included insufficient stratification and geographical coverage. Interactions with decision-makers were commonplace and informed modelling scope and, vice versa, supported recommendations. Conversely, fewer than half of the studies shared open-access code. Interpretation We highlight the evolving ...