Searching Vaccination Strategy with Surrogate-assisted Evolutionary Computing

Zong-De Jian, Tsan-Sheng Hsu, Da-Wei Wang

2016

Abstract

Agent-based stochastic simulation is an established approach to study infectious diseases. Its advantage is the flexibility to incorporate important concepts. The effect of various mitigation strategies has been demonstrated using simulation models. Most of the previous studies compared a few options with a few selected scenarios. We propose to use genetic algorithms to search for the best vaccination strategy for a given scenario with the simulation program as fitness scorer. Vaccination efficacy varies significantly. Therefore, the real challenge is to find a good strategy without the knowledge of it. The simulation software is efficient, yet still takes three minutes to complete a simulation run with Taiwan population. We use surrogate to speed up the search about 1000 times. The surrogate has the average of the absolute value of error around 0.284 percent and the rank correlation coefficient is greater than 0.98 for all the scenarios except one. The optimal solution with surrogate has fitness value very close to use simulations. The difference is generally less than one percent. We envision that an autonomous software searches through the huge scenario space with the help of surrogate function and adaptively executes simulation program to revise the surrogate function to produce higher fidelity surrogate and better search results.

References

  1. Basta, N. E., Halloran, M. E., Matrajt, L., and Longini, I. M. (2008). Estimating influenza vaccine efficacy from challenge and community-based study data. American Jourmal of Epidemiology, 168(12):1343-1352.
  2. Chang, H.-J., Chuang, J.-H., Fu, Y.-C., Hsu, T.-S., Hsueh, C.-W., Tsai, S.-C., and Wang, D.-W. (2015). The impact of household structures on pandemic influenza vaccination priority. In Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, pages 482-487.
  3. Fu, Y.-c., Wang, D.-W., and Chuang, J.-H. (2012). Representative contact diaries for modeling the spread of infectious diseases in taiwan. PLoS ONE, 7(10):1-7.
  4. Germann, T. C., Kadau, K., Longini, I. M., and Macken, C. A. (2006). Mitigation strategies for pandemic influenza in the united states. Proceedings of the National Academy of Sciences, 103(15):5935-5940.
  5. Jin, Y. (2011). Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 2(1):61-70.
  6. J.J. Grefenstette, J. F. (1985). Genetic search with approximate fitness evaluations. In International Conference on Genetic Algorithms and Their Applications, pages 112-120.
  7. Lee, B. Y., Brown, S. T., Korch, G. W., Cooley, P. C., Zimmerman, R. K., Wheaton, W. D., Zimmer, S. M., Grefenstette, J. J., Bailey, R. R., Assi, T.-M., and Burke, D. S. (2010). A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 { H1N1} influenza pandemic. Vaccine, 28(31):4875-4879.
  8. Loshchilov, I., Schoenauer, M., and Sebag, M. (2010). Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Kraków, Poland, September 11-15, 2010, Proceedings, Part I, chapter Comparison-Based Optimizers Need ComparisonBased Surrogates, pages 364-373. Springer Berlin Heidelberg, Berlin, Heidelberg.
  9. Meltzer, M. I., Cox, N. J., and Fukuda, K. (1999). The economic impact of pandemic influenza in the United States: priorities for intervention. Emerging Infect. Dis., 5(5):659-671.
  10. Tsai, M.-T., Chern, T.-C., Chuang, J.-H., Hsueh, C.-W., Kuo, H.-S., Liau, C.-J., Riley, S., Shen, B.-J., Shen, C.-H., Wang, D.-W., and Hsu, T.-S. (2010). Efficient simulation of the spatial transmission dynamics of influenza. PLoS ONE, 5(11):1-8.
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Paper Citation


in Harvard Style

Jian Z., Hsu T. and Wang D. (2016). Searching Vaccination Strategy with Surrogate-assisted Evolutionary Computing . In Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-199-1, pages 56-63. DOI: 10.5220/0005958600560063


in Bibtex Style

@conference{simultech16,
author={Zong-De Jian and Tsan-Sheng Hsu and Da-Wei Wang},
title={Searching Vaccination Strategy with Surrogate-assisted Evolutionary Computing},
booktitle={Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2016},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005958600560063},
isbn={978-989-758-199-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Searching Vaccination Strategy with Surrogate-assisted Evolutionary Computing
SN - 978-989-758-199-1
AU - Jian Z.
AU - Hsu T.
AU - Wang D.
PY - 2016
SP - 56
EP - 63
DO - 10.5220/0005958600560063