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All Journal The Journal of Experimental Life Sciences (JELS)
Ririn Nirmalasari
Brawijaya University
Biochemistry, Genetics & Molecular Biology Immunology & microbiology Medicine & Pharmacology Neuroscience

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The Artificial Bee Colony (ABC) Algorithm for Estimating Parameter of Epidemic Influenza Model Ririn Nirmalasari; Agus Suryanto; Syaiful Anam
The Journal of Experimental Life Science Vol. 10 No. 1 (2020)
Publisher : Postgraduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1048.636 KB) | DOI: 10.21776/ub.jels.2019.010.01.06

Abstract

The Artificial Bee Colony (ABC) is one of the stochastic algorithms that can be applied to solve many real-world optimization problems. In this paper, The ABC algorithm was used to estimate the parameter of the epidemic influenza model. This model consists of a differential system represented by variations of Susceptible (S), Exposed (E), Recovered (R), and Infected (I). The ABC processes explore the minimum value of the mean square error function in the current iteration to estimate the unknown parameters of the model. Estimating parameters were made using participation data containing influenza disease in Australia, 2017. The best parameter chosen from the ABC process matched the dynamical behavior of the influenza epidemic field data used. Graphical analysis was used to validate the model. The result shows that the ABC algorithm is efficient for estimating the parameter of the epidemic influenza model. Keywords: ABC, Epidemic, Estimate, Influenza, Parameter.
Co-Authors Agus Suryanto Anam, Syaiful