Findings from Stellenbosch University Provides New Data about Computational Biology (Calibration of individual-based models to epidemiological data: A systematic review)

2020 MAY 21 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Fresh data on Biology - Computational Biology are presented in a new report. According to news reporting from Stellenbosch, South Africa, by NewsRx journalists, research stated, “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.”

Financial support for this research came from Research Foundation - Flanders.

The news correspondents obtained a quote from the research from Stellenbosch University, “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.”

According to the news reporters, the research concluded: “There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures.”

For more information on this research see: Calibration of individual-based models to epidemiological data: A systematic review. Plos Computational Biology, 2020;16(5):e1007893. (Public Library of Science - www.plos.org; Plos Computational Biology - www.ploscompbiol.org)

Our news journalists report that additional information may be obtained by contacting J. Dushoff, South African Dept. of Science and Technology-National Research Foundation (DST-NRF) South African Centre for Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa. Additional authors for this research include C.M. Hazelbag, E.M. Dominic, Z.E. Mthombothi and W. Delva.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1371/journal.pcbi.1007893. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

(Our reports deliver fact-based news of research and discoveries from around the world.)