Prediksi Penyebaran COVID-19 dengan SIR Model

Main Article Content

Fransina Natasha Cleopatra Repi
Viktor Vekky Ronald Repi

Abstract

Coronavirus (COVID-19) adalah pandemi yang telah menyerang lebih dari 170 negara di seluruh dunia. Jumlah pasien yang terinfeksi dan meninggal telah meningkat dengan kecepatan yang mengkhawatirkan di hampir semua negara yang terkena dampak. Pemerintah di seluruh dunia telah dipaksa untuk mengambil keputusan kritis dan sulit sebagai langkah untuk menahan penyebaran penyakit. Teknik peramalan memainkan peran yang sangat penting dalam menghasilkan prediksi yang akurat. Pada penelitian ini menggunakan model Susceptible Infected and Recovered (SIR) yaitu mengkategorikan teknik peramalan menjadi dua jenis, yaitu teori stokastik / model matematika dan teknik sains data / pembelajaran mesin. Data yang dikumpulkan dari platform open source juga penting dalam peramalan. Dalam penelitian ini data set yaitu diakses dari data Base WHO. Hasil perhitungan menunjukkan bahwa perbandingan model dan semua kumpulan data memiliki R0 > 0.98 yang tinggi. Perhitungan model dari awal wabah hingga 5 Mei 2020 diungkapkan. Simulasi memperkirakan wabah akan berakhir pada tanggal 9 Juni 2020 dimana puncak wabah terjadi pada tanggal 20 April 2020. Simulasi hingga 19 Mei 2020 memperkirakan wabah akan berakhir pada tanggal 29 Juni 2020 dimana puncak wabah terjadi pada 20 Mei 2020.

Article Details

How to Cite
Repi, F. N. C., & Repi, V. V. R. (2023). Prediksi Penyebaran COVID-19 dengan SIR Model. Jurnal Ilmiah Giga, 26(1), 46–59. https://doi.org/10.47313/jig.v26i1.2706
Section
Articles

References

World Health Organization, “No Title.” https://www.who.int/emergencies/diseases/novel-coronavirus-2019/#

S. N. DeWitte, “Mortality risk and survival in the aftermath of the medieval Black Death,” PLoS One, vol. 9, no. 5, 2014, doi: 10.1371/journal.pone.0096513.

N. M. Frieden, “The Russian cholera epidemic, 1892-93, and medical professionalization.,” J. Soc. Hist., vol. 10, no. 4, pp. 538–559, Jun. 1977, doi: 10.1353/jsh/10.4.538.

J. M. Ponnighaus and S. M. Oxborrow, “Construction projects and spread of HIV,” Lancet, vol. 336, no. 8724, p. 1198, Nov. 1990, doi: 10.1016/0140-6736(90)92821-X.

S. Dixon, S. McDonald, and J. Roberts, “AIDS and economic growth in Africa: a panel data analysis,” J. Int. Dev., vol. 13, no. 4, pp. 411–426, May 2001, doi: 10.1002/jid.795.

C. Achonu, A. Laporte, and M. A. Gardam, “The financial impact of controlling a respiratory virus outbreak in a teaching hospital: lessons learned from SARS.,” Can. J. Public Health, vol. 96, no. 1, pp. 52–54, 2005, doi: 10.1007/BF03404018.

M. Tizzoni et al., “Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm,” BMC Med., vol. 10, no. 1, p. 165, 2012, doi: 10.1186/1741-7015-10-165.

S. J. Fong, G. Li, N. Dey, R. Gonzalez-Crespo, and E. Herrera-Viedma, “Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak,” Int. J. Interact. Multimed. Artif. Intell., vol. 6, no. 1, p. 132, 2020, doi: 10.9781/ijimai.2020.02.002.

D. GREENHALGH and G. HAY, “Mathematical modelling of the spread of HIV/AIDS amongst injecting drug users,” Math. Med. Biol. A J. IMA, vol. 14, no. 1, pp. 11–38, Mar. 1997, doi: 10.1093/imammb/14.1.11.

M. A. Khan and A. Atangana, “Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative,” Alexandria Eng. J., pp. 2379–2389, 2020, doi: 10.1016/j.aej.2020.02.033.

J. Wangping et al., “Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China,” Front. Med., vol. 7, 2020, doi: 10.3389/fmed.2020.00169.

B. Hu and J. Gong, “Support Vector Machine based classification analysis of SARS spatial distribution,” Proc. - 2010 6th Int. Conf. Nat. Comput. ICNC 2010, vol. 2, no. Icnc, pp. 924–927, 2010, doi: 10.1109/ICNC.2010.5583921.

N. Sultana and N. Sharma, “Statistical Models for Predicting Swine F1u Incidences in India,” in 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 134–138. doi: 10.1109/ICSCCC.2018.8703300.

Ismael Abdulrahman, “SimCOVID : Open-Source Simulation Programs for the COVID-19 Outbreak,” medRxiv Prepr., 2020, doi: https://doi.org/10.1101/2020.04.13.20063354.

R. Sameni, “Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus,” arXiv:2003.11371v3, pp. 1–17, 2020, [Online]. Available: http://arxiv.org/abs/2003.11371

A. A. Toda, “Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact,” arXiv:2003.11221v2, pp. 1–15, 2020, [Online]. Available: http://arxiv.org/abs/2003.11221

E. M. and H. R. and L. R.-G. and C. A. and C. G. and J. H. and B. M. and S. D. and D. B. and E. O.-O. and M. Roser, “Coronavirus Pandemic (COVID-19),” Our World in Data, 2020. https://ourworldindata.org/coronavirus

M. Batista, “Estimation of the final size of the second phase of the coronavirus epidemic by the logistic model,” medRxiv Prepr., pp. 1–13, 2020, doi: https://doi.org/10.1101/2020.03.11.20024901.

M. Batista, “Estimation of coronavirus COVID-19 epidemic evaluation by the SIR model,” 2020. https://www.mathworks.com/matlabcentral/fileexchange/74658-fitviruscovid19). (accessed Jul. 01, 2020).

“No Title,” 2020. https://covid19.who.int/table

“No Title.” https://covid19.go.id/p/berita/analisis-data-covid-19-indonesia-update-20-desember-2020