Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a survey-based modellers’ perspective from four European countries
Van Kleef E., Van Bortel W., Arsevska E., Busani L., Dellicour S., Di Domenico L., Gilbert M., van Elsland S., Kraemer M., Lai S., Lemey P., Merler S., Milosavljevic Z., Rizzoli A., Simic D., Tatem A., Teisseire M., Wint W., Colizza V., Poletto C.
Background: Advanced outbreak analytics were instrumental in informing governmental decision-making during the COVID-19 pandemic. However, combined and 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 European modelling practices, data usage, gaps, and engagement between modellers and decision-makers to inform future global epidemic-intelligence. 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 policy impact. Results: Our sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modelling 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 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 geographic 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 non-traditional data availability 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.