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Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

Author

Listed:
  • Theresa Reiker

    (Swiss Tropical and Public Health Institute
    University of Basel)

  • Monica Golumbeanu

    (Swiss Tropical and Public Health Institute
    University of Basel)

  • Andrew Shattock

    (Swiss Tropical and Public Health Institute
    University of Basel)

  • Lydia Burgert

    (Swiss Tropical and Public Health Institute
    University of Basel)

  • Thomas A. Smith

    (Swiss Tropical and Public Health Institute
    University of Basel)

  • Sarah Filippi

    (Imperial College London)

  • Ewan Cameron

    (University of Oxford
    Curtin University
    Perth Children’s Hospital)

  • Melissa A. Penny

    (Swiss Tropical and Public Health Institute
    University of Basel)

Abstract

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.

Suggested Citation

  • Theresa Reiker & Monica Golumbeanu & Andrew Shattock & Lydia Burgert & Thomas A. Smith & Sarah Filippi & Ewan Cameron & Melissa A. Penny, 2021. "Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27486-z
    DOI: 10.1038/s41467-021-27486-z
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    References listed on IDEAS

    as
    1. C Marijn Hazelbag & Jonathan Dushoff & Emanuel M Dominic & Zinhle E Mthombothi & Wim Delva, 2020. "Calibration of individual-based models to epidemiological data: A systematic review," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-17, May.
    2. Ewan Cameron & Katherine E. Battle & Samir Bhatt & Daniel J. Weiss & Donal Bisanzio & Bonnie Mappin & Ursula Dalrymple & Simon I. Hay & David L. Smith & Jamie T. Griffin & Edward A. Wenger & Philip A., 2015. "Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria," Nature Communications, Nature, vol. 6(1), pages 1-10, November.
    3. Jamie T. Griffin & Neil M. Ferguson & Azra C. Ghani, 2014. "Estimates of the changing age-burden of Plasmodium falciparum malaria disease in sub-Saharan Africa," Nature Communications, Nature, vol. 5(1), pages 1-10, May.
    4. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
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    Cited by:

    1. Pablo M. Salazar & Alice Kamau & Aurelien Cavelan & Samuel Akech & Arthur Mpimbaza & Robert W. Snow & Melissa A. Penny, 2024. "Severe outcomes of malaria in children under time-varying exposure," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. Vahab Vahdat & Oguzhan Alagoz & Jing Voon Chen & Leila Saoud & Bijan J. Borah & Paul J. Limburg, 2023. "Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks," Medical Decision Making, , vol. 43(6), pages 719-736, August.

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