The XPRIZE Pandemic Response Challenge addressed the problem of predicting future trends of the pandemic in different regions and countries under non-pharmaceutical interventions (NPI), and prescribing Pareto efficient NPI that compromise between the number of new cases and the cost of intervention. Herein, we present the models and algorithms developed by the Alphanumerics Team during the Pandemic Response Challenge... The algorithms are based on an \textit{optimal state estimation} and \textit{finite horizon optimal control} formulation of the problem. Our major contributions were to develop a predictor and optimal prescriptor over an \textit{ad hoc} compartmental model for tracking the variations of the new cases with an \textit{extended Kalman filter/smoother}. The algorithm provides both prediction and prescription, provides performance bounds and requires extremely low computational resources. The solution is theoretically proved to be an optimal balance between the NPI, within the scope of the presented compartmental model. The NPI found by this algorithm are on the \textit{Pareto front} of the bi-objective optimization space and enable strategists to select the desired balance between the human factor and NPI cost by modifying a single parameter. The parameters of the model, which correspond to the human interaction model and recovery rates, can be trained by using machine learning techniques. The algorithm is applied on historic global data from the Oxford COVID-19 Government Response Tracker project and compared versus random and fixed interventions. MATLAB implementations of the developed algorithms are available online. (read more)