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Calibration of individual-based models to epidemiological data : a systematic review

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Abstract
Individual-based models (IBMs) informing public health policy should be calibrated to data and provide estimates of uncertainty. Two main components of model-calibration methods are the parameter-search strategy and the goodness-of-fit (GOF) measure; many options exist for each of these. This review provides an overview of calibration methods used in IBMs modelling infectious disease spread. We identified articles on PubMed employing simulation-based methods to calibrate IBMs informing public health policy in HIV, tuberculosis, and malaria epidemiology published between 1 January 2013 and 31 December 2018. Articles were included if models stored individual-specific information, and calibration involved comparing model output to population-level targets. We extracted information on parameter-search strategies, GOF measures, and model validation. The PubMed search identified 653 candidate articles, of which 84 met the review criteria. Of the included articles, 40 (48%) combined a quantitative GOF measure with an algorithmic parameter-search strategy–either an optimisation algorithm (14/40) or a sampling algorithm (26/40). These 40 articles varied widely in their choices of parameter-search strategies and GOF measures. For the remaining 44 (52%) articles, the parameter-search strategy could either not be identified (32/44) or was described as an informal, non-reproducible method (12/44). Of these 44 articles, the majority (25/44) were unclear about the GOF measure used; of the rest, only five quantitatively evaluated GOF. Only a minority of the included articles, 14 (17%) provided a rationale for their choice of model-calibration method. Model validation was reported in 31 (37%) articles. Reporting on calibration methods is far from optimal in epidemiological modelling studies of HIV, malaria and TB transmission dynamics. The adoption of better documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures.
Keywords
Ecology, Modelling and Simulation, Computational Theory and Mathematics, Genetics, Ecology, Evolution, Behavior and Systematics, Molecular Biology, Cellular and Molecular Neuroscience, SIMULATION-MODELS, UNCERTAINTY ANALYSIS, ECONOMIC-EVALUATION, INFERENCE, STRATEGIES, CARE

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MLA
Hazelbag, C. Marijn, et al. “Calibration of Individual-Based Models to Epidemiological Data : A Systematic Review.” PLOS COMPUTATIONAL BIOLOGY, vol. 16, no. 5, 2020, doi:10.1371/journal.pcbi.1007893.
APA
Hazelbag, C. M., Dushoff, J., Dominic, E. M., Mthombothi, Z. E., Delva, W., Kouyos, R. D., & Pitzer, V. E. (2020). Calibration of individual-based models to epidemiological data : a systematic review. PLOS COMPUTATIONAL BIOLOGY, 16(5). https://doi.org/10.1371/journal.pcbi.1007893
Chicago author-date
Hazelbag, C. Marijn, Jonathan Dushoff, Emanuel M. Dominic, Zinhle E. Mthombothi, Wim Delva, Roger Dimitri Kouyos, and Virginia E. Pitzer. 2020. “Calibration of Individual-Based Models to Epidemiological Data : A Systematic Review.” PLOS COMPUTATIONAL BIOLOGY 16 (5). https://doi.org/10.1371/journal.pcbi.1007893.
Chicago author-date (all authors)
Hazelbag, C. Marijn, Jonathan Dushoff, Emanuel M. Dominic, Zinhle E. Mthombothi, Wim Delva, Roger Dimitri Kouyos, and Virginia E. Pitzer. 2020. “Calibration of Individual-Based Models to Epidemiological Data : A Systematic Review.” PLOS COMPUTATIONAL BIOLOGY 16 (5). doi:10.1371/journal.pcbi.1007893.
Vancouver
1.
Hazelbag CM, Dushoff J, Dominic EM, Mthombothi ZE, Delva W, Kouyos RD, et al. Calibration of individual-based models to epidemiological data : a systematic review. PLOS COMPUTATIONAL BIOLOGY. 2020;16(5).
IEEE
[1]
C. M. Hazelbag et al., “Calibration of individual-based models to epidemiological data : a systematic review,” PLOS COMPUTATIONAL BIOLOGY, vol. 16, no. 5, 2020.
@article{8664794,
  abstract     = {Individual-based models (IBMs) informing public health policy should be calibrated to data and provide estimates of uncertainty. Two main components of model-calibration methods are the parameter-search strategy and the goodness-of-fit (GOF) measure; many options exist for each of these. This review provides an overview of calibration methods used in IBMs modelling infectious disease spread. We identified articles on PubMed employing simulation-based methods to calibrate IBMs informing public health policy in HIV, tuberculosis, and malaria epidemiology published between 1 January 2013 and 31 December 2018. Articles were included if models stored individual-specific information, and calibration involved comparing model output to population-level targets. We extracted information on parameter-search strategies, GOF measures, and model validation. The PubMed search identified 653 candidate articles, of which 84 met the review criteria. Of the included articles, 40 (48%) combined a quantitative GOF measure with an algorithmic parameter-search strategy–either an optimisation algorithm (14/40) or a sampling algorithm (26/40). These 40 articles varied widely in their choices of parameter-search strategies and GOF measures. For the remaining 44 (52%) articles, the parameter-search strategy could either not be identified (32/44) or was described as an informal, non-reproducible method (12/44). Of these 44 articles, the majority (25/44) were unclear about the GOF measure used; of the rest, only five quantitatively evaluated GOF. Only a minority of the included articles, 14 (17%) provided a rationale for their choice of model-calibration method. Model validation was reported in 31 (37%) articles. Reporting on calibration methods is far from optimal in epidemiological modelling studies of HIV, malaria and TB transmission dynamics. The adoption of better documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures.},
  articleno    = {e1007893},
  author       = {Hazelbag, C. Marijn and Dushoff, Jonathan and Dominic, Emanuel M. and Mthombothi, Zinhle E. and Delva, Wim and Kouyos, Roger Dimitri and Pitzer, Virginia E.},
  issn         = {1553-734X},
  journal      = {PLOS COMPUTATIONAL BIOLOGY},
  keywords     = {Ecology,Modelling and Simulation,Computational Theory and Mathematics,Genetics,Ecology,Evolution,Behavior and Systematics,Molecular Biology,Cellular and Molecular Neuroscience,SIMULATION-MODELS,UNCERTAINTY ANALYSIS,ECONOMIC-EVALUATION,INFERENCE,STRATEGIES,CARE},
  language     = {eng},
  number       = {5},
  pages        = {17},
  title        = {Calibration of individual-based models to epidemiological data : a systematic review},
  url          = {http://dx.doi.org/10.1371/journal.pcbi.1007893},
  volume       = {16},
  year         = {2020},
}

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