PARAMETER ESTIMATION OF COVID-19 COMPARTMENT MODEL IN INDONESIA USING PARTICLE SWARM OPTIMIZATION
Downloads
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.
Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2020.138817
Chen, X., Li, J., Xiao, C., & Yang, P. (2020). Numerical solution and parameter estimation for uncertain SIR model with application to COVID-19. Fuzzy Optimization and Decision Making. https://doi.org/10.1007/s10700-020-09342-9
Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons and Fractals, 139, 110057. https://doi.org/10.1016/j.chaos.2020.110057
Cotta, R. M., Naveira"cotta, C. P., & Magal, P. (2020). Mathematical parameters of the COVID"19 epidemic in Brazil and evaluation of the impact of different public health measures. Biology. https://doi.org/10.3390/biology9080220
Darti, I., Suryanto, A., Panigoro, H. S., & Susanto, H. (2020). Forecasting COVID-19 epidemic in Spain and Italy using a generalized Richards model with quantified uncertainty. Commun. Biomath. Sci, 3, 90–100.
Domingo, E., & Perales, C. (2021). The time for COVID-19 vaccination. Journal of Virology. https://doi.org/10.1128/jvi.02437-20
Eberhart, R. C., & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000. https://doi.org/10.1109/CEC.2000.870279
Frenck Jr, R. W., Klein, N. P., Kitchin, N., Gurtman, A., Absalon, J., Lockhart, S., ... Bailey, R. (2021). Safety, immunogenicity, and efficacy of the BNT162b2 Covid-19 vaccine in adolescents. New England Journal of Medicine.
Ghostine, R., Gharamti, M., Hassrouny, S., & Hoteit, I. (2021). An extended seir model with vaccination for forecasting the covid-19 pandemic in saudi arabia using an ensemble kalman filter. Mathematics. https://doi.org/10.3390/math9060636
Gómez, C. E., Perdiguero, B., & Esteban, M. (2021). Emerging SARS-CoV-2 variants and impact in global vaccination programs against SARS-CoV-2/COVID-19. Vaccines. https://doi.org/10.3390/vaccines9030243
Knoll, M. D., & Wonodi, C. (2021). Oxford–AstraZeneca COVID-19 vaccine efficacy. The Lancet, 397(10269), 72–74.
López, L., & Rodó, X. (2021). A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: Simulating control scenarios and multi-scale epidemics. Results in Physics, 21. https://doi.org/10.1016/j.rinp.2020.103746
Mahase, E. (2020). Covid-19: Vaccine candidate may be more than 90% effective, interim results indicate. BMJ, 371. https://doi.org/https://doi.org/10.1136/bmj.m4347
Musafir, R. R., Suryanto, A., & Darti, I. (2021). Dynamics of COVID-19 epidemic model with asymptomatic infection, quarantine, protection and vaccination. Communication in Biomathematical Sciences, 4(2), 106–124. https://doi.org/10.5614/cbms.2021.4.2.3
Ndii, M. Z., Hadisoemarto, P., Agustian, D., & Supriatna, A. (2020). An analysis of covid-19 transmission in indonesia and saudi arabia. Commun. Biomath. Sci, 3(1), 19–27.
Nurlaila, I., Hidayat, A. A., & Pardamean, B. (2021). Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia. IOP Conference Series: Earth and Environmental Science, 729(1), 12002.
Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., ... Zerbini, C. (2020). Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. New England Journal of Medicine, 383(27), 2603–2615.
Postnikov, E. B. (2020). Estimation of COVID-19 dynamics "on a back-of-envelope”: Does the simplest SIR model provide quantitative parameters and predictions? Chaos, Solitons and Fractals, 135, 109841. https://doi.org/10.1016/j.chaos.2020.109841
Rayungsari, M., Aufin, M., & Imamah, N. (2020). Parameters estimation of generalized Richards Model for COVID-19 cases in Indonesia using genetic algorithm. Jambura Journal of Biomathematics (JJBM), 1(1), 25–30. https://doi.org/10.34312/jjbm.v1i1.6910
Riyapan, P., Shuaib, S. E., & Intarasit, A. (2021). A mathematical model of COVID-19 pandemic: a case study of Bangkok, Thailand. Computational and Mathematical Methods in Medicine, 2021, 6664483. https://doi.org/10.1155/2021/6664483
Shabir, S., & Singla, R. (2016). A comparative study of genetic algorithm and the particle swarm optimization. Int. J. Electr. Eng, 9(2), 215–223.
Shen, M., Zu, J., Fairley, C. K., Pagán, J. A., An, L., Du, Z., ... Zhang, L. (2021). Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use. Vaccine. https://doi.org/10.1016/j.vaccine.2021.02.056
Sonawane, K., Troisi, C. L., & Deshmukh, A. A. (2021). COVID-19 vaccination in the UK: Addressing vaccine hesitancy. The Lancet Regional Health - Europe. https://doi.org/10.1016/j.lanepe.2020.100016
Tosepu, R., Gunawan, J., Effendy, D. S., Lestari, H., Bahar, H., & Asfian, P. (2020). Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of The Total Environment, 725, 138436.
Wirawan, G. B. S., Mahardani, P. N. T. Y., Cahyani, M. R. K., Laksmi, N. L. P. S. P., & Januraga, P. P. (2021). Conspiracy beliefs and trust as determinants of COVID-19 vaccine acceptance in Bali, Indonesia: cross-sectional study. Personality and Individual Differences, 110995.
Wu, K., Darcet, D., Wang, Q., & Sornette, D. (2020). Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world. Nonlinear Dynamics, 101(3), 1561–1581. https://doi.org/10.1007/s11071-020-05862-6
Yadav, S., Kumar, D., Singh, J., & Baleanu, D. (2021). Analysis and dynamics of fractional order Covid-19 model with memory effect. Results in Physics, 24, 104017. https://doi.org/https://doi.org/10.1016/j.rinp.2021.104017
Zhang, Z., Gul, R., & Zeb, A. (2020). Global sensitivity analysis of COVID-19 mathematical model. Alexandria Engineering Journal, 60(1), 565–572. https://doi.org/10.1016/j.aej.2020.09.035
Zreiq, R., Kamel, S., Boubaker, S., A Al-Shammary, A., D Algahtani, F., & Alshammari, F. (2020). Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm. AIMS Public Health. https://doi.org/10.3934/publichealth.2020064
- Every manuscript submitted to must observe the policy and terms set by the Jurnal Berkala Epidemiologi
- Publication rights to manuscript content published by the Jurnal Berkala Epidemiologi is owned by the journal with the consent and approval of the author(s) concerned. (download copyright agreement)
- Complete texts of electronically published manuscripts can be accessed free of charge if used for educational and research purposes according to copyright regulations.
JBE by Universitas Airlangga is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
