Objective To develop an algorithm (sCOVID) to predict the risk of severe complications of COVID-19 in a community-dwelling population to optimise vaccination scenarios. Design Population-based cohort study. Setting 264 Dutch general practices contributing to the NL-COVID database. Participants 6074 people aged 0–99 diagnosed with COVID-19. Main outcomes Severe complications (hospitalisation, institutionalisation, death). The algorithm was developed from a training data set comprising 70% of the patients and validated in the remaining 30%. Potential predictor variables included age, sex, chronic comorbidity score (CCS) based on risk factors for COVID-19 complications, obesity, neighbourhood deprivation score (NDS), first or second COVID-19 wave and confirmation test. Six population vaccination scenarios were explored: (1) random ( naive ), (2) random for persons above 60 years ( 60plus ), (3) oldest patients first in age band of 5 years ( oldest first ), (4) target population of the annual influenza vaccination programme ( influenza ), (5) those 25–65 years of age first ( worker ), and (6) risk based using the prediction algorithm ( sCOVID ). Results Severe complications were reported in 243 (4.8%) people with 59 (20.3%) nursing home admissions, 181 (62.2%) hospitalisations and 51 (17.5%) deaths. The algorithm included age, sex, CCS, NDS, wave and confirmation test (c-statistic=0.91, 95% CI 0.88 to 0.94) in the validation set. Applied to different vaccination scenarios, the proportion of people needed to be vaccinated to reach a 50% reduction of severe complications was 67.5%, 50.0%, 26.1%, 16.0%, 10.0% and 8.4% for the worker, naive , influenza, 60plus, oldest first and sCOVID scenarios, respectively. Conclusion The sCOVID algorithm performed well to predict the risk of severe complications of COVID-19 in the first and second waves of COVID-19 infections in this Dutch population. The regression estimates can and need to be adjusted for future predictions. The algorithm can be applied to identify persons with ...