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

[tetano]

• Esther van Kleef[SUP]1,2,3[/SUP] , Wim Van Bortel[SUP]3[/SUP] , Elena Arsevska[SUP]4[/SUP] , Luca Busani[SUP]5[/SUP] , Simon Dellicour[SUP]6,7,8[/SUP] , Laura Di Domenico[SUP]9[/SUP] , Marius Gilbert[SUP]6[/SUP] , Sabine L van Elsland[SUP]10[/SUP] , Moritz UG Kraemer[SUP]11[/SUP] , Shengjie Lai[SUP]12[/SUP] , Philippe Lemey[SUP]13[/SUP] , Stefano Merler[SUP]14[/SUP] , Zoran Milosavljevic[SUP]15[/SUP] , Annapaola Rizzoli[SUP]16[/SUP] , Danijela Simic[SUP]15[/SUP] , Andrew J Tatem[SUP]12[/SUP] , Maguelonne Teisseire[SUP]17[/SUP] , William Wint[SUP]18[/SUP] , Vittoria Colizza[SUP]19[/SUP] , Chiara Poletto[SUP]20[/SUP]

​ BACKGROUND

Advanced 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.

AIM

This study assessed the evolution of modelling practices, data usage, gaps, and engagement between modellers and decision-makers to inform future global epidemic intelligence.

METHODS

We 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.

RESULTS

Our 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.

CONCLUSIONS

We 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.



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https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2025.30.42.2500216