DOI: https://doi.org/10.14710/jtsiskom.2021.14222

Comparison of various epidemic models on the COVID-19 outbreak in Indonesia

Research Center of Informatics, National Research and Innovation Agency. Jl. Sangkuriang, Komplek LIPI Gedung 20 Lt. 3, Bandung 40135, Indonesia

Received: 24 May 2021; Revised: 26 Jul 2021; Accepted: 23 Jan 2021; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 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 paper compares four mathematical models to describe Indonesia's current coronavirus disease 2019 (COVID-19) pandemic. The daily confirmed case data are used to develop the four models: Logistic, Richards, SIR, and SEIR. A least-square fitting computes each parameter to the available confirmed cases data. We conducted parameterization and sensitivity experiments by varying the length of the data from 60 until 300 days of transmission. All models are susceptible to the epidemic data. Though the correlations between the models and the data are pretty good (>90%), all models still show a poor performance (RMSE>18%). In this study case, Richards model is superior to other models from the highest projection of the positive cases of COVID-19 in Indonesia. At the same time, others underestimate the outbreak and estimate too early decreasing phase. Richards model predicts that the pandemic remains high for a long time, while others project the pandemic will finish much earlier.
Fulltext View|Download Email colleagues
Keywords: COVID-19; SEIR; SIR; Richard; Logistic
Funding: National Research and Innovation Agency

Article Metrics:

Article Info
Section: Original Research Articles
Language : EN
Related articles
Combining the NER-OCR methods to improve information retrieval efficiency in the Indonesian posters Sistem Pakar Untuk Identifikasi Dan Alternatif Solusi Terhadap Permasalahan Yang Dihadapi Peserta Didik Sekolah Menengah Menggunakan Rule-Based Machine Learning Pembuatan Aplikasi Permainan Pengenalan Provinsi di Indonesia Melalui Game “Adventure Indonesia” Berbasis Android Carotid Artery Detection in B-Mode Ultrasound Images Based on Convolution Neural Network Single Shot Multibox Detector Decision support system model using FIS Mamdani for determining tire pressure More related articles
Most cited articles
Perancangan Permainan “UangAndro” Sebagai Media Pembelajaran Pengenalan Mata Uang Rupiah Pada Anak Berbasis Android Pengembangan Sistem Informasi Rekam Medis untuk Dinas Kabupaten Grobogan Perancangan Sistem Manajemen Restoran dengan Aplikasi Pemesanan Restoran Berbasis Mobile dalam Jaringan Lokal Discrimination of civet coffee using visible spectroscopy Human Vital Physiological Parameters Monitoring: A Wireless Body Area Technology Based Internet of Things More cited articles
  1. Z. A. Putra and S. A. Z. Abidin, “Application of seir model in COVID-19 and the effect of lockdown on reducing the number of active cases,” Indonesian Journal of Science and Technology, vol. 5, no. 2, pp.185-192, 2020. doi: 10.17509/ijost.v5i2.24432
  2. A. L. Kapetanovi'c and D. Poljak, “Modeling the epidemic outbreak and dynamics of COVID-19 in Croatia,” in the International Conference on Smart and Sustainable Technologies, Split, Croatia, Sep. 2020, pp. 1-5. doi: 10.23919/SpliTech49282.2020.9243757
  3. D. Giuliani, M. M. Dickson, and G. Espa, F. Santi, “Modelling and predicting the spatio-temporal spread of coronavirus disease 2019 (COVID-19) in Italy,” Available at SSRN 3559569, Mar. 2020. doi: 10.2139/ssrn.3559569
  4. 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
  5. L. Peng, W. Yang, D. Zhang, C. Zhuge, and L. Hong, “Epidemic analysis of COVID-19 in China by dynamical modeling,” 2020, arXiv:2002.06563. doi: 10.48550/arXiv.2002.06563
  6. S. He, Y. Peng, and K. Sun, “SEIR modeling of the COVID-19 and its dynamics,” Nonlinear Dynamics, vol. 101, no. 3, pp. 1667-1680, 2020. doi: 10.1007/s11071-020-05743-y
  7. E. Soewono, “On the analysis of covid-19 transmission in Wuhan, Diamond Princess and Jakarta-cluster,” Communication in Biomathematical Sciences, vol. 3, no. 1, pp. 9–18, 2020. doi: 10.5614/cbms.2020.3.1.2
  8. 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
  9. K. Roosa et al., “Short-term forecasts of the covid-19 epidemic in Guangdong and Zhejiang, China: February 13–23, 2020,” Journal of Clinical Medicine, vol. 9, no. 2, 596, 2020. doi: 10.3390/jcm9020596
  10. C. Y. Shen, “Logistic growth modelling of covid-19 proliferation in China and its international implications,” International Journal of Infectious Diseases, vol. 96, pp. 582–589, 2020. doi: 10.1016/j.ijid.2020.04.085
  11. A. M. Almeshal, A. I. Almazrouee, M. R. Alenizi, and S. N. Alhajeri, “Forecasting the spread of COVID-19 in Kuwait using compartmental and logistic regression models,” Applied Sciences, vol. 10, no. 10, 3402, 2020. doi: 10.3390/app10103402
  12. J. F. Medina-Mendieta, M. Cortés-Cortés, and M. Cortés-Iglesias, “COVID-19 Forecasts for Cuba using logistic regression and Gompertz curves,” MEDICC Review, vol. 22, no. 3, pp. 32-39, 2020. doi: 10.37757/MR2020.V22.N3.8
  13. B. Malavika, S. Marimuthu, M. Joy, A. Nadaraj, E. S. Asirvatham, and L. Jeyaseelan, “Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models,” Clinical Epidemiology and Global Health, vol. 9, pp. 26-33, 2020. doi: 10.1016/j.cegh.2020.06.006
  14. K. Wu, D. Darcet, Q. Wang, and D. Sornette, “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, vol. 101, no. 3, pp. 1561-1581, 2020. doi: 10.1007/s11071-020-05862-6
  15. P. Wang, X. Zheng, J. Li, and B. Zhu, “Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics,” Chaos, Solitons Fractals, vol. 139, 110058, 2020. doi: 10.1016/j.chaos.2020.110058
  16. 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. doi: 10.5614/cbms.2020.3.1.1
  17. D. G. Kleinbaum, L. L. Kupper, and L. E. Chambless. “Logistic regression analysis of epidemiologic data: theory and practice,” Communications in Statistics-Theory and Methods, vol. 11, no. 5, pp. 485–547, 1982. doi: 10.1080/03610928208828251
  18. R. Jin, F. Yan, and J. Zhu, “Application of logistic regression model in an epidemiological study,” Science Journal of Applied Mathematics and Statistics, vol. 3, no. 5, pp. 225–229, 2015. doi: 10.11648/j.sjams.20150305.12
  19. J. S. Cramer, “The early origins of the logic model,” Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, vol. 35, no. 4, pp. 613-626. 2004. doi: 10.1016/j.shpsc.2004.09.003
  20. A. A. King, M. Domenech de Cellès, F. M. Magpantay, and P. Rohani. “Avoidable errors in the modelling of outbreaks of emerging pathogens with special reference to Ebola,” in Proceedings of the Royal Society B: Biological Sciences, vol. 282, no. 1806, 20150347, 2015. doi: 10.1098/rspb.2015.0347
  21. S. Y. Lee, B. Lei, and B. Mallick, “Estimation of COVID-19 spread curves integrating global data and borrowing information,” PLoS ONE, vol. 15, no. 7, e0236860, 2020. doi: 10.1371/journal.pone.0236860
  22. F. J. Richards, “A flexible growth function for empirical use,” Journal of Experimental Botany, vol. 10, no. 2, pp. 290–301, 1959. doi: 10.1093/jxb/10.2.290
  23. W. O. Kermack and A. G. McKendrick, “A contribution to the mathematical theory of epidemics,” in Proceedings of the Royal Society of London. Series A, Containing papers of a mathematical and physical character, vol. 115, no. 772, pp. 700–721, 1927. doi: 10.1098/rspa.1927.0118
  24. T. Harko, F. S. Lobo, and M. K. Mak, “Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates,” Applied Mathematics and Computation, vol. 236, pp. 184–194, 2014. doi: 10.1016/j.amc.2014.03.030
  25. J. A. P. Heesterbeek, “A brief history of R0 and a recipe for its calculation,” Acta Biotheoretica, vol. 50, no. 3, pp. 189–204, 2002. doi: 10.1023/A:1016599411804
  26. M. S. Nixon and A. S. Aguado, “Chapter 11 – Appendix 2: Least squares analysis”, in Feature Extraction and Image Processing for Computer Vision, 3rd ed., Academic Press, 2012, pp. 519-523. doi: 10.1016/B978-0-12-396549-3.00017-3
  27. X. Luo, H. Duan, and K. Xu, “A novel grey model based on traditional Richards model and its application in COVID-19”, Chaos Solitons Fractals, vol. 142, 2021. doi: 10.1016/j.chaos.2020.110480
  28. Satuan Tugas Penanganan COVID-19, “Perkembangan testing nasional jadikan bahan evaluasi,” Dec. 2020. [Online]. Available: https://covid19.go.id/p/berita/perkembangan-testing-nasional-jadikan-bahan-evaluasi [Accessed: March 11, 2021]

Last update:

No citation recorded.

Last update: 2024-11-20 22:01:41

No citation recorded.