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


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
05 2020
Historique:
received: 05 09 2019
accepted: 21 04 2020
revised: 21 05 2020
pubmed: 12 5 2020
medline: 29 8 2020
entrez: 12 5 2020
Statut: epublish

Résumé

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.

Identifiants

pubmed: 32392252
doi: 10.1371/journal.pcbi.1007893
pii: PCOMPBIOL-D-19-01519
pmc: PMC7241852
doi:

Banques de données

Dryad
['10.5061/dryad.8sf7m0cj6']

Types de publication

Journal Article Research Support, Non-U.S. Gov't Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1007893

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

C Marijn Hazelbag (CM)

South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.

Jonathan Dushoff (J)

South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
Department of Biology, Department of Mathematics and Statistics, Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.

Emanuel M Dominic (EM)

South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.

Zinhle E Mthombothi (ZE)

South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.

Wim Delva (W)

South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.
Center for Statistics, I-BioStat, Hasselt University, Diepenbeek, Belgium.
Department of Global Health, Faculty of Medicine and Health, Stellenbosch University, Stellenbosch, South Africa.
International Centre for Reproductive Health, Ghent University, Ghent, Belgium.
Rega Institute for Medical Research, KU Leuven, Leuven, Belgium.

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