LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Andrianakis, I.; Vernon, I.; McCreesh, N.; McKinley, T. J.; Oakley, J. E.; Nsubuga, R. N.; Goldstein, M.; White, R. G. (2017)
Publisher: Wiley-Blackwell
Types: Article
Subjects: Original Article, Gaussian processes, Stochastic simulators, Original Articles, Inverse problems, Individualā€based models, Calibration
Summary Complex stochastic models are commonplace in epidemiology, but their utility depends on their calibration to empirical data. History matching is a (pre)calibration method that has been applied successfully to complex deterministic models. In this work, we adapt history matching to stochastic models, by emulating the variance in the model outputs, and therefore accounting for its dependence on the model's input values. The method proposed is applied to a real complex epidemiological model of human immunodeficiency virus in Uganda with 22 inputs and 18 outputs, and is found to increase the efficiency of history matching, requiring 70% of the time and 43% fewer simulator evaluations compared with a previous variant of the method. The insight gained into the structure of the human immunodeficiency virus model, and the constraints placed on it, are then discussed.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Andrianakis, I., Vernon, I., McCreesh, N., McKinley, T. J., Oakley, J. E., Nsubuga, R., Goldstein, M. and White, R. G. (2015) Bayesian history matching and calibration of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda. PLOS Computnl Biol., 11, 1-18.
    • Andrieu, C., Doucet, A. and Holenstein, R. (2010) Particle Markov chain Monte Carlo methods (with discussion). J. R. Statist. Soc. B, 72, 269-342.
    • Ankenman, B., Nelson, B. and Staum, J. (2010) Stochastic kriging for simulation metamodeling. Ops Res., 58, 371-382.
    • Bastos, L. S. and O'Hagan, A. (2009) Diagnostics for Gaussian process emulators. Technometrics, 51, 425-438.
    • Bellan, S., Fiorella, K. J., Melesse, D., Getz, W., Williams, B. and Dushoff, J. (2013) Extra-couple HIV transmission in sub-Saharan Africa: a mathematical modelling study of survey data. Lancet, 381, 1561-1569.
    • Boukouvalas, A., Cornford, D. and Stehlik, M. (2014) Optimal design for correlated processes with inputdependent noise. Computnl Statist. Data Anal., 71, 1088-1102.
    • Brynjarsdottir, J. and O'Hagan, A. (2014) Learning about physical parameters: the importance of model discrepancy. Invrs. Prob., 30, no. 11, article 114007.
    • Conti, S. and O'Hagan, A. (2010) Bayesian emulation of complex multi-output and dynamic computer models. J. Statist. Planng Inf., 140, 640-651.
    • Craig, P. S., Goldstein, M., Seheult, A. H. and Smith, J. A. (1997) Pressure matching for hydrocarbon reservoirs: a case study in the use of Bayes linear strategies for large computer experiments (with discussion). In Case Studies in Bayesian Statistics (eds C. Gatsonis, J. S. Hodges, R. E. Kass, R. E. McCulloch, P. Rossi and N. D. Singpurwalla), vol. III, pp. 37-93. New York: Springer.
    • Fedorov, V. and Hackl, P. (1997) Model-oriented Design of Experiments. Berlin: Springer.
    • Fricker, T. E., Oakley, J. E. and Urban, N. M. (2013) Multivariate Gaussian process emulators with nonseparable covariance structures. Technometrics, 55, 47-56.
    • Gibson, G. J. and Renshaw, E. (1998) Estimating parameters in stochastic compartmental models using Markov chain methods. IMA J. Math. Appl. Med. Biol., 15, 19-40.
    • Goldstein, M. and Rougier, J. (2009) Reified Bayesian modelling and inference for physical systems. J. Statist. Planng Inf., 139, 1221-1239.
    • Goldstein, M., Seheult, A. and Vernon, I. (2013) Assessing model adequacy. In Environmental Modelling: Finding Simplicity in Complexity, 2nd edn (eds J. Wainwright and M. Mulligan). Chichester: Wiley.
    • Granich, R., Gilks, C., Dye, C., De Cock, K. M. and Williams, B. G. (2009) Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet, 373, 48-57.
    • Grimm, V., Berger, U., Bastiansen, F., Eliassen, S. and Ginot, V. (2006) A standard protocol for describing individual-based and agent-based models. Ecol. Modllng, 198, 115-126.
    • Henderson, D. A., Boys, R. J., Krishnan, K. J., Lawless, C. and Wilkinson, D. J. (2009) Bayesian emulation and calibration of a stochastic computer model of mitochondrial DNA deletions in substantia nigra neurons. J. Am. Statist. Ass., 104, 76-87.
    • Kennedy, M. C. and O'Hagan, A. (2001) Bayesian calibration of computer models (with discussion). J. R. Statist. Soc. B, 63, 425-464.
    • Loeppky, J. L., Sacks, J. and Welch, W. J. (2009) Choosing the sample size of a computer experiment: a practical guide. Technometrics, 51, 366-376.
    • May, R. M. (2004) Uses and abuses of mathematics in biology. Science, 303, 790-793.
    • McCreesh, N., O'Brien, K., Nsubuga, R. N., Shafer, L. A., Bakker, R., Seeley, J., Hayes, R. J. and White, R. G. (2012) Exploring the potential impact of a reduction in partnership concurrency on HIV incidence in rural Uganda: a modeling study. Sexlly Transmttd Dis., 39, 407-413.
    • McKinley, T. J., Cook, A. R. and Deardon, R. (2009) Inference in epidemic models without likelihoods. Int. J. Biostatist., 5, no. 1, article 24.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects

  • RCUK | Calibration and analysis o...

Cite this article