| Title: |
Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers' perspective, 2020 to 2022 |
| Authors: |
van Kleef, E.; Van Bortel, W.; Arsevska, E.; Busani, L.; Dellicour, S.; Di Domenico, L.; Gilbert, M.; van Elsland, S. L; Kraemer, M. U.; Lai, S.; Lemey, P.; Merler, S.; Milosavljevic, Z.; Rizzoli, A.; Simic, D.; Tatem, A. J.; Teisseire, M.; Wint, W.; Colizza, V.; Poletto, C. |
| Contributors: |
Van Kleef, E.; Van Bortel, W.; Arsevska, E.; Busani, L.; Dellicour, S.; Di Domenico, L.; Gilbert, M.; Van Elsland, S.L.; Kraemer, M.U.; Lai, S.; Lemey, P.; Merler, S.; Milosavljevic, Z.; Rizzoli, A.; Simic, D.; Tatem, A.J.; Teisseire, M.; Wint, W.; Colizza, V.; Poletto, C. |
| Publication Year: |
2025 |
| Collection: |
Fondazione Edmund Mach: IRIS-OpenPub |
| Subject Terms: |
Enhanced infectious disease surveillance; Modelling; Outbreak analytics; Pandemic decision-making; Science-policy interface; Settore MVET-03/A - Malattie infettive degli animali |
| Description: |
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.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 |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/41133306; info:eu-repo/semantics/altIdentifier/wos/WOS:001613804600001; volume:30; issue:42; journal:EUROSURVEILLANCE; https://hdl.handle.net/10449/93998 |
| DOI: |
10.2807/1560-7917.ES.2025.30.42.2500216 |
| Availability: |
https://hdl.handle.net/10449/93998; https://doi.org/10.2807/1560-7917.ES.2025.30.42.2500216 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.96E3BE97 |
| Database: |
BASE |