This paper accounts in lives-saved partial unlock strategies that may be used to facilitate reopening economies that have been shut down due to an epidemic or pandemic. For this purpose it introduces a new approach to simulation using an internal SIR engine with seasonality, and external behavior forcing calibrated with case data to account for initial human behavior under social distancing. The overall method relies on public goal setting and both professional and public feedback behavior. In this way it avoids much of the chaotic sensitivity to parameters and divergence of predictions and behavior which undermine the public image of epidemiology models and create rebounds. We study reducing the total cases by controlling threshold overshoot as economies reopen, controlling medical resource utilization, and reducing economic shutdown duration, all of these across significant scenario variation. We provide a quantitative analysis of overshoot and demonstrate a two-step manual method as well as the feedback method of avoiding it. We show goal-managed partial unlock to manage critical resources has the consequential effects of reducing economic downtime and bringing the cumulative cases down about 9%-27%, thereby saving lives with some degree of certainty. The optimization of overshoot does leave some risk of creating a residual small infection existing on birth rate and migration, and we provide some guidelines for minimizing the risk. Effectiveness is demonstrated using COVID-19 actual data and parameters for other diseases with replication factors up to 15.