This paper investigates the importance of the age composition for pandemic policy design. To do so, it introduces an economic framework with age heterogeneity, individual choice, and incomplete information, emphasizing the value of testing. Calibrating the model to the US Covid-19 pandemic reveals an 80% reduction in the death toll due to voluntary actions and the lockdown implemented in the US. The optimal lockdown, however, is more stringent than what was implemented. Moreover, the social planner follows an asymmetric approach by locking down the young relatively more than the old. We highlight the role of testing in reducing deaths, lowering economic costs, and allowing for a less restrictive lockdown. A social planner would choose a faster ramp-up in testing capacity relative to what occurred in the US. We use the framework to provide insights into pandemics caused by different viruses (including the Spanish flu), and underline the influence of economic conditions on optimal policies.