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

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 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.

Funding

This work was supported by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Different co-authors acknowledge funding from the Fonds National de la Recherche Scientifique (F.R.S.-FNRS, Belgium; grant n°F.4515.22), from the Research Foundation — Flanders ( Fonds voor Wetenschappelijk Onderzoek — Vlaanderen , FWO, Belgium; grant n°G098321N), LEAPS (grant agreement n°101094685), Belgian Science Policy research grant BE-PIN (TD/231/BE-PIN), the Department of Economy, Science and Innovation of the Flemish government, the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), Horizon Europe grant ESCAPE (101095619), and ANR grant DATAREDUX (ANR-19-CE46-0008-03), the Rockefeller Foundation (PC-2022-POP-005), Google.org, the Oxford Martin School Programmes in Pandemic Genomics & Digital Pandemic Preparedness, European Union’s Horizon Europe programme project E4Warning (#101086640), Wellcome Trust grants 303666/Z/23/Z, 226052/Z/22/Z & 228186/Z/23/Z, the United Kingdom Research and Innovation (#APP8583), the Medical Research Foundation (MRF-RG-ICCH-2022-100069), UK International Development (301542-403), the Bill & Melinda Gates Foundation (INV-063472) and Novo Nordisk Foundation (NNF24OC0094346), Cariparo Foundation through the program Starting Package. The funders had no role in the manuscript. The authors declare no conflicts of interest.

Box: Research in context

Evidence before this study

We conducted a systematic search in PubMed and MEDLINE between January 2020 and March 2024, using keywords related to SARS-CoV-2 (“COVID-19” OR “SARS-CoV-2” OR “2019-nCoV” OR “coronavirus”), mathematical modelling (model* AND “Mathematical” OR “Epidemiological” OR “Statistical” OR “Computational” OR “Bioinformatics”), and decision-making (“Policy” OR “Decision-making”) and no geographical limitations other than redistricting our search to English language. This search yielded 1,971 studies, of which 40 papers were shortlisted for full-text review, and 18 were in line with the scope of our study and one was identified through snowballing. These prior studies primarily concerned individual countries and/or qualitative accounts from predominantly earlier phases based on scoping reviews, interviews or published commentaries written by scientists involved in the COVID-19 response. Prior studies highlighted lessons learned related to relevant modelling frameworks and their data requirements, as well as data sharing and availability challenges, the importance of ensuring transparency, reproducibility and model validation for cross-country analyses, communicating model uncertainty and outcomes to policy-makers, and the role of structured collaboration between scientists and decision-makers.

Added value of this study

Our study provides the first systematic and quantitative evaluation of evolving data, analytical techniques, and science-policy interactions of COVID-19 modelling for pandemic decision-making across multiple EU/EEA countries and pandemic phases through EU-funded partnerships. We present an evaluation and analytical framework that combines all components which previous studies addressed in part, and can be adapted to other geographical contexts and future threat scenarios.

Implications of all the available evidence

The Lancet Commission on Strengthening the Use of Epidemiological Modelling of Emerging and Pandemic Infectious Diseases is currently examining the policy, technical and communications challenges inherent in modelling future threats. Our results help shape recommendations and inform governmental pandemic preparedness investments. Notably, by documenting collective memory on where and how enhanced surveillance data collection and collaborative analytical networks can strengthen modelling for pandemic decision-making, we contribute to improving effective responses to future health crises across the EU/EEA region and globally.