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Background In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. Methods We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. Results Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. Conclusions Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion. This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (PC and NH, grant number 682540 – TransMID project, PL, NH, PB grant number 101003688 – EpiPose project). SA and NH gratefully acknowledge support from the Fonds voor Wetenschappelijk Onderzoek (FWO) (RESTORE project – G0G2920N). LW received funding from the Research Foundation Flanders (1234620N). PL received funding from the Research Foundation Flanders (post-doctoral grant 1242021N). The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government. We thank several researchers from the SIMID COVID-19 consortium from the University of Antwerp and Hasselt University for numerous constructive discussions and meetings. We thank Giulia Pullano, Laura Di Domenico and Vittoria Colizza for useful discussions. The authors are also very grateful for access to the data from the Belgian Scientific Institute for Public Health, Sciensano.