Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/49750
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dc.contributor.authorBAGAFORO, Roel Jude-
dc.contributor.authorDUPAS, Marie-Cécile-
dc.contributor.authorABRAMS, Steven-
dc.contributor.authorDellicour, Simon-
dc.contributor.authorHENS, Niel-
dc.date.accessioned2026-08-11T12:49:03Z-
dc.date.available2026-08-11T12:49:03Z-
dc.date.issued2026-
dc.date.submitted2026-08-08T11:56:45Z-
dc.identifier.citationArchives of public health, 84 (1) (Art N° 160)-
dc.identifier.urihttp://hdl.handle.net/1942/49750-
dc.description.abstractBackground: The COVID-19 pandemic underscored the importance of integrating human behaviour in infectious disease modelling approaches, yet an in-depth assessment of how behavioural components are incorporated remains limited. We conducted a scoping review of COVID-19 models applied to Belgian data to examine how be-havioural dynamics, both voluntary and policy-driven, were represented within model structures. Our aim was to identify current practices, highlight methodological gaps, and provide recommendations for the development of behaviourally integrated epidemiological models. Methods: Using Scopus and PubMed, we identified 98 studies published between March 2020 and October 2024, describing 105 models in total. Models were classified by model class (mathematical, statistical, or ensemble), objectives, approaches used to incorporate behavioural factors, and types of behaviour data employed. Results: Behavioural integration was confined to specific modelling contexts, with only half of the 105 models incorporating behavioural components. Mechanistic models , particularly compartmental models, were the most likely to include behavioural features, especially in studies assessing non-pharmaceutical interventions or conducting long-term forecasts and scenario analyses. Behavioural change was most commonly represented through modifications to transmission parameters or contact matrices. These adjustments were frequently informed by social contact surveys or mobility data derived from various sources. Conclusions: In contrast to previous reviews that focused exclusively on behavioural models, this study evaluates the full landscape of Belgian COVID-19 models, offering a comprehensive perspective on how behavioural representation varies across modelling approaches. Our findings recommend that effective behavioural integration relies on timely, routine, and disaggregated surveillance and behaviour data, alongside the use of flexible mechanistic models.-
dc.description.sponsorshipThis research was funded by the VacxSYS project (G0A4624N), supported by the Research Foundation – Flanders (FWO; Fonds Wetenschappelijk Onderzoek), and by the Belgian Pandemic Intelligence Network (TD/231/BE-PIN), funded by the Belgian Science Policy Office (BELSPO). UHasselt Project Numbers: Project R-14684 and R-14518-
dc.language.isoen-
dc.publisher-
dc.rightsOpen Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.-
dc.subject.otherSARS-CoV-2-
dc.subject.otherbehaviour-
dc.subject.otherepidemiological dynamics-
dc.subject.othernon-pharmaceutical interventions-
dc.subject.otherpharmaceutical interventions-
dc.subject.othermechanistic models-
dc.subject.othermathematical models-
dc.subject.otherstatistical models-
dc.subject.otherensemble models-
dc.titleA scoping review of COVID-19 modelling studies in Belgium 2020-2024: incorporation of behaviour and lessons learned-
dc.typeJournal Contribution-
dc.identifier.issue1-
dc.identifier.volume84-
local.bibliographicCitation.jcatA1-
local.type.refereedRefereed-
local.type.specifiedReview-
local.bibliographicCitation.artnr160-
dc.identifier.doi10.1186/s13690-026-01959-3-
dc.identifier.isiWOS:001825295500001-
local.provider.typeCrossRef-
local.uhasselt.internationalno-
item.contributorBAGAFORO, Roel Jude-
item.contributorDUPAS, Marie-Cécile-
item.contributorABRAMS, Steven-
item.contributorDellicour, Simon-
item.contributorHENS, Niel-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.fullcitationBAGAFORO, Roel Jude; DUPAS, Marie-Cécile; ABRAMS, Steven; Dellicour, Simon & HENS, Niel (2026) A scoping review of COVID-19 modelling studies in Belgium 2020-2024: incorporation of behaviour and lessons learned. In: Archives of public health, 84 (1) (Art N° 160).-
crisitem.journal.issn0778-7367-
crisitem.journal.eissn2049-3258-
Appears in Collections:Research publications
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