Assessing the role of children in the COVID-19 pandemic in Belgium using perturbation analysis
Year: 2025
Type: article
Abstract: Understanding the evolving role of different age groups in virus transmission dynamics is essential for informed pandemic management. This study delves into the age-related transmission patterns of SARS-CoV-2 in Belgium from November 2020 to February 2022. We employed a next-generation matrix approach, integrating longitudinal social contact data and numerical simulations of the evolving population susceptibility. A perturbation analysis of the effective reproduction number (Rt ) underscored the age-specific transmission patterns. From November to December 2020, adults aged [18,60) were main contributors to Rt (≈ 78%), with children aged [0,12) having a marginal role (≈ 3.7%). This pattern shifted between January and March 2021, coinciding with in-person education resumption and the Alpha variant emergence; children’s contribution to Rt increased to ≈ 38%. Stringent measures in March 2021 significantly reduced transmission levels, substantially downsizing the role of the [18,30) age group. Following the summer school break, in September-October 2021, we observed a notable resurgence in children’s contribution to Rt . Our findings highlight the noteworthy and varying influence of the [0,12) age group on SARS-CoV-2 transmission, offering insights to design nuanced pandemic responses, e.g., that balance public health needs with socio-educational implications of interventions like extended school closures. Overall, this study demonstrates the effectiveness of our methodology in uncovering age-specific transmission patterns in the study of infectious disease spread.
Source: Nature Communications
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: London School of Hygiene and Tropical Medicine, European Commission, Fonds Wetenschappelijk Onderzoek, Universiteit Antwerpen, Universiteit Hasselt, Vlaamse regering, Horizon 2020 Framework Programme, Fundação para a Ciência e a Tecnologia, Bijzonder Onderzoeksfonds UGent, Janssen Pharmaceuticals
Awards: G113523436, Efficient and rapidly SCAlable EU-wide evidence-driven Pandemic response plans through dynamic Epidemic data assimilation, Public health decision making with stochastic individual-based models: a translational framework driven by advances in health economics, model inference and reinforcement learning (ACCELERATE), G3068456498, G434337789, Epidemic intelligence to minimize 2019-nCoV’s public health, economic and social impact in Europe, G6045508252, G8642034124, SARS-coV2 variants Evaluation in pRegnancy and paeDIatrics cohorts
OpenAlex ID: W4408218490
