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

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By G.M.

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. 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 use and needs of modelling during public health crises. The reported missing of non-traditional surveillance data, even two years into the pandemic, underscores the need to rethink sustainable data collection and sharing practices, including from non-profit providers. Future preparedness should focus on strengthening collaborative platforms, research consortia and modelling networks to foster data and code sharing and effective collaboration between academia, decision-makers, and data providers.

Source: MedRxIV, https://www.medrxiv.org/content/10.1101/2025.03.12.25323819v1

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