I am doing agent based modeling (ABM) for infectious disease modeling, and the model has 50+ parameters which can any probability value between 0 and 1, or can be any float value from 0 to >0 (continuous scale). The objective is to find a parameter value set that gives the infected case curve of the simulated result to match with the infected case curve of real life. Thus, the ABM simulation can use such parameter set to forecast into the future.
Due to high dimensionality, the search for the best/optimal parameter values can be extremely long to run, rendering it impractical. What are some of the approaches I can use to help screen, evaluate and choose the best parameter set?
I imagine it may require some kind of simulating/testing set data split, cross-validation, and search in parameter space via some automated and efficient algorithms?