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May 25, 2026Archives of Public Health0 citationsOpen Access

A scoping review of COVID-19 modelling studies in Belgium 2020-2024: incorporation of behaviour and lessons learned

RBRoel Jude BagaforoHasselt UniversityMDMarie-Cécile DupasUniversité Libre de BruxellesSASteven AbramsUniversity of Antwerp

Key Points

  • This review examines how human behaviour has been integrated into COVID-19 modelling in Belgium from 2020 to 2024.
  • Conducted a scoping review of 98 studies published between March 2020 and October 2024, describing 105 models.
  • Classified models by class (mathematical, statistical, ensemble), objectives, methods of incorporating behaviour, and types of behaviour data used.
  • Analyzed the representation of behavioural components in the models.
  • Only 50% of the 105 models incorporated behavioural components.
  • Mechanistic (particularly compartmental) models were more likely to include behavioural features, especially in assessing non-pharmaceutical interventions.
  • Behavioural changes were mainly represented by adjustments to transmission parameters or contact matrices, informed by social contact surveys and mobility data.

Abstract

Abstract Background 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 behavioural 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.

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Cite This Study

Bagaforo et al. (2026) studied this question.

synapsesocial.com/papers/6a13e8030e02ee3982d32a50https://doi.org/10.1186/s13690-026-01959-3
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