ABSTRACT
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 0. If 0 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. (English) [ FROM AUTHOR] Latar Belakang: Baru-baru ini pemerintah menetapkan program vaksinasi untuk mengatasi tingginya kasus reaktif COVID-19 di Indonesia. Metode estimasi parameter yang baik diperlukan untuk menghasilkan prediksi yang akurat dari dinamika model kompartemen penyebaran COVID-19. Tujuan: Penelitian ini dilakukan untuk mengaplikasikan Particle Swarm Optimization untuk mendapatkan parameter dari model kompartemen Susceptible-Vaccinated-Infected-Recovered untuk kasus COVID-19. Metode: Penelitian ini dilakukan pada bulan April-Mei 2020 di Indonesia dengan desain penelitian eksperimen. Penelitian ini menggunakan data kasus COVID-19 di Indonesia melalui laman covid19.go.id. Perangkat data tersebut memuat banyaknya kasus reaktif, tervaksinasi, dan sembuh. Data tersebut digunakan untuk mengestimasi parameter dari model kompartemen COVID-19. Metode estimasi yang digunakan adalah Particle Swarm Optimization. Hasil penelitian berupa simulasi numerik yang didukung oleh program Matlab. Hasil: Penelitian menunjukkan bahwa parameter yang diestimasi memiliki nilai yang cukup baik karena mean square error cukup kecil jika dibandingkan dengan data yang digunakan. Kasus reaktif COVID-19 mengalami penurunan hingga 21 Agustus 2021. Pada waktu selanjutnya, grafik kasus reaktif COVID-19 akan mengalami kenaikan hingga akhir tahun 2021. Hal ini disebabkan oleh laju infeksi virus populasi tervaksinasi masih bernilai positif, 0 . Apabila terjadi 0 di titik stasioner, maka secara matematis, kasus reaktif COVID-19 akan mengalami penurunan. Kesimpulan: Metode Particle Swarm Optimization dapat mengestimasi parameter dengan baik berdasarkan mean square error dan grafik yang dapat mendeskripsikan perilaku kasus COVID-19 dan solusi dari fenomena yang terjadi di masa depan. (Indonesian) [ FROM AUTHOR] Copyright of Jurnal Berkala Epidemiologi is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)