skip to main content

Prediksi puncak pandemi Covid-19 di Indonesia dengan model SIR

Prediction of the peak Covid-19 pandemic in Indonesia using SIR model

Department of Informatics, Universitas Telkom. Jl. Telekomunikasi Terusan Buah Batu, Bandung 40257, Indonesia

Received: 25 Aug 2020; Revised: 30 Nov 2020; Accepted: 7 Dec 2020; Available online: 18 Dec 2020; Published: 31 Jan 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
This research implements the Susceptible, Infected, and Removed (SIR) model to predict the Covid-19 outbreak in Indonesia. The government official data, consisting of infected, dead, and recovered, are used as actual data to interpolate the model through matching data with minimum mean squared error (MSE). The study uses one of the Quasi-Newton search methods, the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) algorithm, to determine the interaction coefficient's optimal value in the model with the minimum MSE value. Based on data as of July 18, 2020, it predicts that the peak of the infected number will be in October 2020 with around 14 % of the total population infected, and the MSE of 18.42 is relative to the period of the actual data. Meanwhile, the basic reproduction rate is calculated to be 2.035 from the model, where it is underestimated by about 29 % compared to the relative basic reproduction rate from the provided actual data.
Keywords: SIR model; BFGS; MSE; pandemic prediction; Covid-19
Funding: Universitas Telkom

Article Metrics:

  1. Kementrian Kesehatan Republik Indonesia, “Pedoman Pencegahan dan Pengendalian Coronavirus Disease (Covid-19) - Rev 4,” Kementrian Kesehatan Republik Indonesia, 2020. [Online]. Available: https://www.kemkes.go.id/. [Accessed: Nov. 30, 2020]
  2. Kementerian Sekretariat Negara RI, “Penetapan bencana nonalam penyebaran corona virus disease 2019 (COVID -19) sebagai bencana nasional,” Kementrian Kementerian Sekretariat Negara RI, KEPPRES No 12 Tahun 2020, 2020. [Online]. Available: https://jdih.setneg.go.id/Produk. [Accessed: Nov. 30, 2020]
  3. Worldometers, "Covid-19 Coronavirus Pandemic", Worldometers.info, July 2020. [Online]. Available: https://www.worldometers.info/coronavirus/. [Accessed: July. 18, 2020]
  4. W. O. Kermack and A. G. McKendrick, "A contribution to the mathematical theory of epidemics," in Proceedings of the Royal Society A, Mathematical, Physical and Engineering Sciences, vol. 115, no. 772, pp. 700-721, 1927. doi: 10.1098/rspa.1927.0118
  5. L. Yakob and A. Clements, "A mathematical model of chikungunya dynamics and control: the major epidemic on Réunion ssland," PLoS ONE, vol. 8, no. 3, pp. e57448, 2013. doi: 10.1371/journal.pone.0057448
  6. J. Jafaruddin, S. Indratno, N. Nuraini, A. Supriatna, and E. Soewono, "Estimation of the basic reproductive ratio for dengue fever at the take-off period of dengue infection," Computational and Mathematical Methods in Medicine, vol. 2015, pp. 1-14, 2015. doi: 10.1155/2015/206131
  7. Y. Hsieh, J. Lee, and H. Chang, "SARS epidemiology modeling," Emerging Infectious Diseases, vol. 10, no. 6, pp. 1165-1167, 2004. doi: 10.3201/eid1006.031023
  8. H. Adamu, M. Muhammad, A. M.Jingi, and M. Usman, "Mathematical modelling using improved SIR model with more realistic assumptions," International Journal of Engineering and Applied Sciences, vol. 6, no. 1, pp. 64-69, 2019. doi: 10.31873/ijeas.6.1.22
  9. S. Djilali and B. Ghanbari, "Coronavirus pandemic: A predictive analysis of the peak outbreak epidemic in South Africa, Turkey, and Brazil,"Chaos, Solitons & Fractals, vol. 138, 109971, 2020. doi: 10.1016/j.chaos.2020.109971
  10. R. Salgotra, M. Gandomi, and A. Gandomi, "Time series analysis and forecast of the covid-19 pandemic in india using genetic programming," Chaos, Solitons & Fractals, vol. 138, 109945, 2020. doi: 10.1016/j.chaos.2020.109945
  11. M. Yousaf, S. Zahir, M. Riaz, S. Hussain, and K. Shah, "Statistical analysis of forecasting Covid-19 for upcoming month in Pakistan," Chaos, Solitons & Fractals, vol. 138, 109926, 2020. doi: 10.1016/j.chaos.2020.109926
  12. Z. Yang et al., "Modified SEIR and AI prediction of the epidemics trend of Covid-19 in China under public health interventions," Journal of Thoracic Disease, vol. 12, no. 3, pp. 165-174, 2020. Available: 10.21037/jtd.2020.02.64
  13. K. Roosa et al., "Real-time forecasts of the Covid-19 epidemic in China from February 5th to February 24th, 2020," Infectious Disease Modelling, vol. 5, pp. 256-263, 2020. Available: 10.1016/j.idm.2020.02.002
  14. D. Fanelli and F. Piazza, "Analysis and forecast of Covid-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, vol. 134, 109761, 2020. doi: 10.1016/j.chaos.2020.109761
  15. Y. Liu, A. Gayle, A. Wilder-Smith, and J. Rocklöv, "The reproductive number of Covid-19 is higher compared to SARS coronavirus," Journal of Travel Medicine, vol. 27, no. 2, taaa021, pp. 1-4, 2020. doi: 10.1093/jtm/taaa021
  16. W. Roda, M. Varughese, D. Han and M. Li, "Why is it difficult to accurately predict the Covid-19 epidemic?," Infectious Disease Modelling, vol. 5, pp. 271-281, 2020. doi: 10.1016/j.idm.2020.03.001
  17. B. Mishra et al., "Covid-19 created chaos across the globe: Three novel quarantine epidemic models," Chaos, Solitons & Fractals, vol. 138, 109928, 2020. doi: 10.1016/j.chaos.2020.109928
  18. A. Godio, F. Pace, and A. Vergnano, "SEIR modeling of the Italian epidemic of SARS-CoV-2 using computational swarm intelligence," International Journal of Environmental Research and Public Health, vol. 17, no. 10, 3535, 2020. doi: 10.3390/ijerph17103535
  19. S. Pathak, A. Maiti, and G. Samanta, "Rich dynamics of an SIR epidemic model," Nonlinear Analysis: Modelling and Control, vol. 15, no. 1, pp. 71-81, 2010. doi: 10.15388/na.2010.15.1.14365
  20. F. Brauer, P. Driessche, and J. Wu, Mathematical Epidemiology. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008
  21. D. Saputro and P. Widyaningsih, "Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method for the parameter estimation on geographically weighted ordinal logistic regression model (GWOLR)," In AIP Conference Proceedings, vol. 1868, 040009, 2017. doi: 10.1063/1.4995124
  22. N. Nuraini, K. Khairudin and M. Apri, "Modeling simulation of Covid-19 in Indonesia based on early endemic data", Communication in Biomathematical Sciences, vol. 3, no. 1, pp. 1-8, 2020

Last update:

No citation recorded.

Last update: 2021-11-29 01:53:40

No citation recorded.