Learning from Simulated World - Surrogates Construction with Deep Neural Network

The deep learning approach has been applied to many domains with success. We use deep learning to construct the surrogate function to speed up simulation based optimization in epidemiology. The simulator is an agentbased stochastic model for influenza and the optimization problem is to find vaccination strategy to minimize the number of infected cases. The optimizer is a genetic algorithm and the fitness function is the simulation program. The simulation is the bottleneck of the optimization process. An attempt to use the surrogate function with table lookup and interpolation was reported before. The preliminary results show that the surrogate constructed by deep learning approach outperforms the interpolation based one, as long as similar cases of the testing set have been available in the training set. The average of the absolute value of relative error is less than 0.7 percent, which is quite close to the intrinsic limitation of the stochastic variation of the simulation software 0.2 percent, and the rank coefficients are all above 0.99 for cases we studied. The vaccination strategy recommended is still to vaccine the school age children first which is consistent with the previous studies. The preliminary results are encouraging and it should be a worthy effort to use machine learning approach to explore the vast parameter space of simulation models in epidemiology.