PARAMETER ESTIMATION OF COVID-19 COMPARTMENT MODEL IN INDONESIA USING PARTICLE SWARM OPTIMIZATION

• Main Authors: Raqqasyi Rahmatullah Musafir (Author), Syaiful Anam (Author)
• Format: Book
• Published: Universitas Airlangga, 2022-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: https://doi.org/10.20473/jbe.V10I32022.283-292; 2301-7171; 2541-092X
• Keywords: covid-19; susceptible-vaccinated-infected-recovered model; parameter estimation; article swarm optimization; Public aspects of medicine; RA1-1270; Infectious and parasitic diseases; RC109-216
• Source: Jurnal Berkala Epidemiologi, Vol 10, Iss 3, Pp 283-292 (2022)

Background: The government established a vaccination program to deal with highly reactive COVID-19 cases in Indonesia. In obtaining accurate predictions of the dynamics of the compartment model of COVID-19 spread, a good parameter estimation technique was required.. Purpose: This research aims to apply Particle Swarm Optimization as a parameter estimation method to obtain parameters value from the Susceptible-Vaccinated-Infected-Recovered compartment model of COVID-19 cases. Methods: This research was conducted in April-May 2020 in Indonesia with exploratory design research. The researchers used the data on COVID-19 cases in Indonesia, which was accessed at covid19.go.id. The data set contained the number of reactive cases, vaccinated cases, and recovered cases. The data set was used to estimate the parameters of the COVID-19 compartment model. The results were shown by numerical simulations that apply to the Matlab program. Results: Research shows that the parameters estimated using Particle Swarm Optimization have a fairly good value because the mean square error is relatively small compared to the data size used. Reactive cases of COVID-19 have decreased until August 21, 2021. Next, reactive cases of COVID-19 will increase until the end of 2021. It is because the virus infection rate of the vaccinated population is positive . If occurs before the stationary point, then the reactive cases of COVID-19 will decrease mathematically. Conclusion: Particle Swarm Optimization methods can estimate parameters well based on mean square error and the graphs that can describe the behavior of COVID-19 cases in the future.