Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria

A dynamical epidemic model optimized using a genetic algorithm and a cross-validation method to overcome the overfitting problem is proposed. The cross-validation procedure is applied so that available data are split into a training subset used to fit the algorithm’s parameters, and a smaller subset...

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Autores principales:	M.T. Rouabah, A. Tounsi, N.E. Belaloui
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	COVID-19 Disease spread modeling Genetic algorithm Cross-validation Algeria Science Q
Acceso en línea:	https://doaj.org/article/2e4cfec7f29c411898eddd28fb10c7f2
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spelling	oai:doaj.org-article:2e4cfec7f29c411898eddd28fb10c7f22021-11-26T04:37:17ZGenetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria2468-227610.1016/j.sciaf.2021.e01050https://doaj.org/article/2e4cfec7f29c411898eddd28fb10c7f22021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2468227621003513https://doaj.org/toc/2468-2276A dynamical epidemic model optimized using a genetic algorithm and a cross-validation method to overcome the overfitting problem is proposed. The cross-validation procedure is applied so that available data are split into a training subset used to fit the algorithm’s parameters, and a smaller subset used for validation. This process is tested on Italy, Spain, Germany, and South Korea cases before being applied to Algeria. Interestingly, our study reveals an inverse relationship between the size of the training sample and the number of generations required in the genetic algorithm. Moreover, the enhanced compartmental model presented in this work has proven to be a reliable tool to estimate key epidemic parameters and the non-measurable asymptomatic infected portion of the susceptible population to establish a realistic nowcast and forecast of the epidemic’s evolution. The model is employed to study the COVID-19 outbreak dynamics in Algeria between February 25th, 2020, and May 24th, 2020. The basic reproduction number and effective reproduction number on May 24th, after three months of the outbreak, are estimated to be 3.78 (95% CI 3.033–4.53) and 0.651 (95% CI 0.539–0.761), respectively. Disease incidence, CFR, and IFR are also calculated. Numerical programs developed for this study are made publicly accessible for reproduction and further use.M.T. RouabahA. TounsiN.E. BelalouiElsevierarticleCOVID-19Disease spread modelingGenetic algorithmCross-validationAlgeriaScienceQENScientific African, Vol 14, Iss , Pp e01050- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 Disease spread modeling Genetic algorithm Cross-validation Algeria Science Q
spellingShingle	COVID-19 Disease spread modeling Genetic algorithm Cross-validation Algeria Science Q M.T. Rouabah A. Tounsi N.E. Belaloui Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria
description	A dynamical epidemic model optimized using a genetic algorithm and a cross-validation method to overcome the overfitting problem is proposed. The cross-validation procedure is applied so that available data are split into a training subset used to fit the algorithm’s parameters, and a smaller subset used for validation. This process is tested on Italy, Spain, Germany, and South Korea cases before being applied to Algeria. Interestingly, our study reveals an inverse relationship between the size of the training sample and the number of generations required in the genetic algorithm. Moreover, the enhanced compartmental model presented in this work has proven to be a reliable tool to estimate key epidemic parameters and the non-measurable asymptomatic infected portion of the susceptible population to establish a realistic nowcast and forecast of the epidemic’s evolution. The model is employed to study the COVID-19 outbreak dynamics in Algeria between February 25th, 2020, and May 24th, 2020. The basic reproduction number and effective reproduction number on May 24th, after three months of the outbreak, are estimated to be 3.78 (95% CI 3.033–4.53) and 0.651 (95% CI 0.539–0.761), respectively. Disease incidence, CFR, and IFR are also calculated. Numerical programs developed for this study are made publicly accessible for reproduction and further use.
format	article
author	M.T. Rouabah A. Tounsi N.E. Belaloui
author_facet	M.T. Rouabah A. Tounsi N.E. Belaloui
author_sort	M.T. Rouabah
title	Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria
title_short	Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria
title_full	Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria
title_fullStr	Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria
title_full_unstemmed	Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria
title_sort	genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of covid-19 in algeria
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/2e4cfec7f29c411898eddd28fb10c7f2
work_keys_str_mv	AT mtrouabah geneticalgorithmwithcrossvalidationbasedepidemicmodelandapplicationtotheearlydiffusionofcovid19inalgeria AT atounsi geneticalgorithmwithcrossvalidationbasedepidemicmodelandapplicationtotheearlydiffusionofcovid19inalgeria AT nebelaloui geneticalgorithmwithcrossvalidationbasedepidemicmodelandapplicationtotheearlydiffusionofcovid19inalgeria
_version_	1718409831436517376

Genetic algorithm with cross-validation-based epidemic model and application to the early diffusion of COVID-19 in Algeria

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