[Dealing with deep uncertainty during a pandemic; make policies adaptive]

This article is a reflection on the covid-19 pandemic and the policy on medical and non-medical e.g. (lock down) measures, and on how we can anticipate earlier on for example effects on education and wellbeing of young people. We show that insights from complexity science are relevant for pandemic policy making and advocate use of resilience indicators, alternative computational models and deep uncertainty modeling. Time series of sick leave can act as resilience indicator in health care and showed large difference between acute care, long term care and mental health care in Dutch covid-19 pandemic. Instead of epidemiology based predict and act models, which mostly turn out to be incorrect, we developed alternative multiscale modelsto simulate interdomain effects. In sum, future pandemics policymaking can profit from adaptive decision making under deep uncertainty.