Archives of public health = Archives belges de sante publique · 2026
A scoping review of COVID-19 modelling studies in Belgium 2020-2024: incorporation of behaviour and lessons learned.
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Bagaforo Roel Jude, Dupas Marie-Cécile, Abrams Steven, Dellicour Simon, Hens Niel
Аннотация
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.
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.