In mid April 2020, with more than 2.5 billion people in the world following social distancing measures due to COVID-19, governments are considering relaxing lock-down. We combined individual clinical risk predictions with epidemic modelling to examine simulations of isolation and exit policies. Methods: We developed a method to include personalised risk predictions in epidemic models based on data science principles. We extended a standard susceptible-exposed-infected-removed (SEIR) model to account for predictions of severity, defined by the risk of an individual needing intensive care in case of infection. We studied example isolation policies using simulations with the risk-extended epidemic model, using COVID-19 data and estimates in France as of mid April 2020 (4 000 patients in ICU, around 7 250 total ICU beds occupied at the peak of the outbreak, 0.5 percent of patients requiring ICU upon infection). We considered scenarios varying in the discrimination performance of a risk prediction model, in the degree of social distancing, and in the severity rate upon infection. Confidence intervals were obtained using an Approximate Bayesian Computation approach. The framework may be used with other epidemic models, with other risk predictions, and for other epidemic outbreaks.