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Int. J. Mol. Sci. 2017, 18(12), 2592; doi:10.3390/ijms18122592

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

1,†,* , 2,†
and
2,3,*
1
College of Mathematics and Statistics, Southwest University, Chongqing 400715, China
2
College of Computer and Information Science, Southwest University, Chongqing 400715, China
3
College of Computer Science, Sichuan University, Chengdu 610065, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 19 October 2017 / Revised: 23 November 2017 / Accepted: 26 November 2017 / Published: 1 December 2017
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Abstract

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. View Full-Text
Keywords: agent-based models; generalized additive model; history matching; particle swarm optimization algorithm agent-based models; generalized additive model; history matching; particle swarm optimization algorithm
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Li, T.; Cheng, Z.; Zhang, L. Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection. Int. J. Mol. Sci. 2017, 18, 2592.

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