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Vaccination strategy is crucial in fighting against the COVID-19 pandemic.Since the supply is limited, contact network-based interventions can be mostpowerful to set an optimal strategy by identifying high-risk individuals orcommunities. However, due to the high dimension, only partial and noisy networkinformation can be available in practice, especially for dynamical systemswhere the contact networks are highly time-variant. Furthermore, numerousmutations of SARS-CoV-2 impact considerably the current infectious probability,requiring real-time network updating algorithms. In this study, we propose asequential network updating approach based on data assimilation techniques tocombine different sources of temporal information. We then prioritise theindividuals with high-degree or high-centrality, obtained from the assimilatednetworks, for vaccination. The assimilation-based approach is compared with thestandard method (based on partially observed networks) and a random selectionstrategy in terms of vaccination effectiveness in a SIR model. The numericalcomparison is first carried out using real-world face-to-face dynamicalnetworks collected in a high school, following by sequential multi-layernetworks, generated relying on the Barabasi-Albert model emulating thedepartment of Computing at Imperial College London in the UK as an example.

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies