Abstract
The deep learning approach has been successfully applied to various disciplines. When using optimization algorithms, there is a need to evaluate the performance of solutions found so far. The simulation system usually serve as the evaluator. However, to speedup the process, an approximation function, called surrogate, can replace the time consuming simulator. We propose to use deep learning to construct the surrogate function in epidemiology. The simulator is an agent-based stochastic model for influenza and the optimization problem is to find vaccination strategy to minimize the number of infected cases or economical impact. The optimizer is a genetic algorithm and the fitness function is the simulation program. An attempt to use the surrogate function with table lookup and interpolation was reported before. The results show that the surrogate constructed by deep learning approach outperforms the interpolation based one for both total case and economical impact. The average of the absolute value of relative error is less than 0.27%, which is quite close to the intrinsic limitation of the stochastic variation of the simulation software 0.2%, and the rank coefficients are all above 0.999. The vaccination strategy recommended is still to vaccine the school age children first which is consistent with the previous studies for minimizing total infected cases. As to minimize economical impact, the priority goes to the middle schoolers then to young working adults The 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.
An earlier extended abstract of this paper appears in [1].
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Acknowledgements
We thank anonymous reviewers for their suggestions. The research is partially funded by the grant of “MOST104-2221-E-001-021-MY3”, and “Multidisciplinary Health Cloud Research Program: Technology Development and Application of Big Health Data. Academia Sinica, Taipei, Taiwan”.
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Jian, ZD., Chang, HJ., Hsu, Ts., Wang, DW. (2019). Applying Deep Learning for Surrogate Construction of Simulation Systems. In: Obaidat, M., Ören, T., Rango, F. (eds) Simulation and Modeling Methodologies, Technologies and Applications . SIMULTECH 2017. Advances in Intelligent Systems and Computing, vol 873. Springer, Cham. https://doi.org/10.1007/978-3-030-01470-4_18
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DOI: https://doi.org/10.1007/978-3-030-01470-4_18
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