Parameter Estimation of Logistic Growth Model for Covid-19 Cases in Lampung Using Particle Swarm Optimization
DOI:
https://doi.org/10.31851/sainmatika.v22i2.18420Keywords:
particle swarm optimization, parameter estimation, covid-19, data-driven modelAbstract
Particle Swarm Optimization (PSO) is an optimization algorithm inspired by the behavior and movements of flocks of animals such as birds, fish, insects. In this study, we implement PSO algorithm to estimate the parameters of a mathematical model depicting population growth in the form logistics curve. The model is fitted to COVID-19 cumulative cases in Lampung Province, Indonesia. Based on the results obtained, PSO shows very good performance in estimating the parameters of the Covid-19 growth curve in Lampung Province, with a Mean Absolute Percentage Error (MAPE) value of all observations of less than 10%. We found that the MAPE decline as the number of particles increases.
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