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Fairchild G, Hickmann KS, Mniszewski SM, et al. Optimizing human activity patterns using global sensitivity analysis. Comput Math Organ Theory. 2014;20(4):394-416doi: 10.1007/s10588-013-9171-0.
Fairchild, G., Hickmann, K. S., Mniszewski, S. M., Del Valle, S. Y., & Hyman, J. M. (2014). Optimizing human activity patterns using global sensitivity analysis. Computational and mathematical organization theory, 20(4), 394-416. https://doi.org/10.1007/s10588-013-9171-0
Fairchild, Geoffrey, et al. "Optimizing human activity patterns using global sensitivity analysis." Computational and mathematical organization theory vol. 20,4 (2014): 394-416. doi: https://doi.org/10.1007/s10588-013-9171-0
Fairchild G, Hickmann KS, Mniszewski SM, Del Valle SY, Hyman JM. Optimizing human activity patterns using global sensitivity analysis. Comput Math Organ Theory. 2014 Dec 01;20(4):394-416. doi: 10.1007/s10588-013-9171-0. PMID: 25580080; PMCID: PMC4286349.
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Comput Math Organ Theory. 2014 Dec 01;20(4):394-416. doi: 10.1007/s10588-013-9171-0.

Optimizing human activity patterns using global sensitivity analysis.

Computational and mathematical organization theory

Geoffrey Fairchild, Kyle S Hickmann, Susan M Mniszewski, Sara Y Del Valle, James M Hyman

Affiliations

  1. Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
  2. Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA.
  3. Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA.

PMID: 25580080 PMCID: PMC4286349 DOI: 10.1007/s10588-013-9171-0

Abstract

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule's regularity for a population. We show how to tune an activity's regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.

Keywords: Agent-based modeling; Bayesian Gaussian process regression; Global optimization; Global sensitivity analysis; Harmony search; Sample entropy

References

  1. Proc Natl Acad Sci U S A. 1991 Mar 15;88(6):2297-301 - PubMed
  2. Am J Physiol. 1994 Apr;266(4 Pt 2):H1643-56 - PubMed
  3. Proc Natl Acad Sci U S A. 2002 May 14;99 Suppl 3:7187-8 - PubMed
  4. Proc Natl Acad Sci U S A. 2004 Sep 21;101(38):13709-14 - PubMed
  5. Am J Physiol Heart Circ Physiol. 2000 Jun;278(6):H2039-49 - PubMed
  6. Nature. 2004 May 13;429(6988):180-4 - PubMed
  7. Nature. 2006 Jan 26;439(7075):462-5 - PubMed
  8. Methods Enzymol. 2004;384:172-84 - PubMed
  9. IEEE Trans Biomed Eng. 2006 Feb;53(2):210-8 - PubMed
  10. Am J Physiol Regul Integr Comp Physiol. 2002 Sep;283(3):R789-97 - PubMed
  11. Eur J Appl Physiol. 2003 May;89(3-4):230-7 - PubMed
  12. IEEE Trans Biomed Eng. 2005 Oct;52(10):1671-80 - PubMed
  13. Hum Factors. 2011 Aug;53(4):403-14 - PubMed
  14. Science. 1983 May 13;220(4598):671-80 - PubMed
  15. PLoS Med. 2007 Jan;4(1):e13 - PubMed
  16. Neurobiol Aging. 2002 Jan-Feb;23(1):23-6 - PubMed
  17. Nature. 2008 Jun 5;453(7196):779-82 - PubMed
  18. Proc Natl Acad Sci U S A. 2006 Apr 11;103(15):5935-40 - PubMed

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