Authors:
Zong-De Jian
1
;
Hung-Jui Chang
2
;
Tsan-sheng Hsu
1
and
Da-Wei Wang
1
Affiliations:
1
Academia Sinica, Taiwan
;
2
Academia Sinica and National Taiwan University, Taiwan
Keyword(s):
Deep Learning, Surrogate, Disease Simulator.
Related
Ontology
Subjects/Areas/Topics:
Agent Based Modeling and Simulation
;
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Complex Systems Modeling and Simulation
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Sensor Networks
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Software and Architectures
;
Symbolic Systems
Abstract:
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 agent-based
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.
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