TY - JOUR
T1 - Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making
T2 - a European survey-based modellers' perspective, 2020 to 2022
AU - van Kleef, Esther
AU - Van Bortel, Wim
AU - Arsevska, Elena
AU - Busani, Luca
AU - Dellicour, Simon
AU - Di Domenico, Laura
AU - Gilbert, Marius
AU - van Elsland, Sabine L
AU - Kraemer, Moritz Ug
AU - Lai, Shengjie
AU - Lemey, Philippe
AU - Merler, Stefano
AU - Milosavljevic, Zoran
AU - Rizzoli, Annapaola
AU - Simic, Danijela
AU - Tatem, Andrew J
AU - Teisseire, Maguelonne
AU - Wint, William
AU - Colizza, Vittoria
AU - Poletto, Chiara
PY - 2025/10
Y1 - 2025/10
N2 - BACKGROUNDAdvanced outbreak analytics were instrumental in informing governmental decision-making during the COVID-19 pandemic. However, systematic evaluations of how modelling practices, data use and science-policy interactions evolved during this and previous emergencies remain scarce.AIMThis study assessed the evolution of modelling practices, data usage, gaps, and engagement between modellers and decision-makers to inform future global epidemic intelligence.METHODSWe conducted a two-stage semiquantitative survey among modellers in a large European epidemic intelligence consortium. Responses were analysed descriptively across early, mid- and late-pandemic phases. We used policy citations in Overton to assess policy impact.RESULTSOur sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modelling initially prioritised understanding epidemic dynamics; evaluating non-pharmaceutical interventions and vaccination impacts later became equally important. Traditional surveillance data (e.g. case line lists) were widely available in near-real time. Conversely, real-time non-traditional data (notably social contact and behavioural surveys) and serological data were frequently reported as lacking. Gaps included poor stratification and incomplete geographical coverage. Frequent bidirectional engagement with decision-makers shaped modelling scope and recommendations. However, fewer than half of the studies shared open-access code.CONCLUSIONSWe highlight the evolving use and needs of modelling during public health crises. Persistent gaps in the availability of non-traditional data underscore the need to rethink sustainable data collection and sharing practices, including from for-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.
AB - BACKGROUNDAdvanced outbreak analytics were instrumental in informing governmental decision-making during the COVID-19 pandemic. However, systematic evaluations of how modelling practices, data use and science-policy interactions evolved during this and previous emergencies remain scarce.AIMThis study assessed the evolution of modelling practices, data usage, gaps, and engagement between modellers and decision-makers to inform future global epidemic intelligence.METHODSWe conducted a two-stage semiquantitative survey among modellers in a large European epidemic intelligence consortium. Responses were analysed descriptively across early, mid- and late-pandemic phases. We used policy citations in Overton to assess policy impact.RESULTSOur sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modelling initially prioritised understanding epidemic dynamics; evaluating non-pharmaceutical interventions and vaccination impacts later became equally important. Traditional surveillance data (e.g. case line lists) were widely available in near-real time. Conversely, real-time non-traditional data (notably social contact and behavioural surveys) and serological data were frequently reported as lacking. Gaps included poor stratification and incomplete geographical coverage. Frequent bidirectional engagement with decision-makers shaped modelling scope and recommendations. However, fewer than half of the studies shared open-access code.CONCLUSIONSWe highlight the evolving use and needs of modelling during public health crises. Persistent gaps in the availability of non-traditional data underscore the need to rethink sustainable data collection and sharing practices, including from for-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.
KW - Humans
KW - COVID-19/epidemiology
KW - Europe/epidemiology
KW - Pandemics/prevention & control
KW - Decision Making
KW - SARS-CoV-2
KW - Information Dissemination
KW - Surveys and Questionnaires
U2 - 10.2807/1560-7917.ES.2025.30.42.2500216
DO - 10.2807/1560-7917.ES.2025.30.42.2500216
M3 - Article
C2 - 41133306
SN - 1560-7917
VL - 30
JO - Eurosurveillance
JF - Eurosurveillance
IS - 42
ER -