Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31443
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Mitchell, Ryan
Cairns, David
Hamami, Dalila
Pollock, Kevin
Shankland, Carron
Contact Email: carron.shankland@stir.ac.uk
Title: Effective use of evolutionary computation to parameterise an epidemiological model
Citation: Mitchell R, Cairns D, Hamami D, Pollock K & Shankland C (2021) Effective use of evolutionary computation to parameterise an epidemiological model. In: Computational Intelligence Methods for Bioinformatics and Biostatistics. Lecture Notes in Computer Science: Lecture Notes in Bioinformatics, 12313. CIBB 2019: 16th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, Bergamo, Italy, 04.09.2019-06.09.2019. Cham, Switzerland: Springer. https://www.springer.com/gp/book/9783030630607
Issue Date: 2021
Series/Report no.: Lecture Notes in Computer Science: Lecture Notes in Bioinformatics, 12313
Conference Name: CIBB 2019: 16th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
Conference Dates: 2019-09-04 - 2019-09-06
Conference Location: Bergamo, Italy
Abstract: Predictive epidemiological models are able to be used most effectively when they have first been shown to fit historical data. Finding the right parameters settings for a model is complex: the system is likely to be noisy, the data points may be sparse, and there may be many inter-related parameters. We apply computational intelligence and data mining techniques in novel ways to investigate this significant problem. We construct an original computational model of human papilloma virus and cervical intraepithelial neoplasia with the ultimate aim of predicting the outcomes of varying control techniques (e.g. vaccination, screening, treatment, quarantine). Two computational intelligence techniques (genetic algorithms and particle swarm optimisation) are used over one- stage and two-stage optimisations for eight real-valued model parameters. Rigorous comparison over a variety of quantitative measures demonstrates the explorative nature of the genetic algorithm (useful in this parameter space to support the modeller). Correlations between parameters are drawn out that might otherwise be missed. Clustering highlights the uniformity of the best genetic algorithm results. Prediction of gender-neutral vaccination with the tuned model suggests elimination of the virus across vaccinated and cross-protected strains, supporting recent Scottish government policy. This preliminary study lays the foundation for more widespread use of computational intelligence techniques in epidemiological modelling.
Status: AM - Accepted Manuscript
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: https://www.springer.com/gp/book/9783030630607

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