Python Model Predicts Covid-19 Cases since Omicron in Indonesia
DOI:
https://doi.org/10.24014/coreit.v9i1.18908Abstract
The proposed work uses Support Vector Regression model to predict the new cases, recovered cases, and deaths cases of covid-19 every day during sub-variant omicron spread in Indonesia. We collected data from June 14, 2022, to August 12, 2022 (60 Days). This model was developed in Python 3.6.6 to get the predictive value of the issues mentioned above up to September 21, 2022. The proposed methodology uses a SVR model with the Radial Basis Function as the kernel and a 10% confidence interval for curve fitting. The data collected has been divided into 2 with a size of 40% test data and 60% training data. Mean Squared Error, Root Mean Squared Error, Regression score, and percentage accuracy calculated the model performance parameters. This model has an accuracy above 87% in predicting new cases and recovered patients and 68% in predicting daily death cases. The results show a Gaussian decrease in the number of cases, and it could take another 4 to 6 weeks for it to drop to the minimum level as the origin of the undiscovered omicron sub-variant. RBF (Radial Basis Function) very efficient and has higher accuracy than linear or polynomial regression as kernel of SVR.References
N. Zhu et al., “A Novel Coronavirus from Patients with Pneumonia in China, 2019,” N Engl J Med, vol. 382, no. 8, pp. 727–733, Feb. 2020, doi: 10.1056/NEJMoa2001017.
S. Boccaletti, W. Ditto, G. Mindlin, and A. Atangana, “Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond,” Chaos Solitons Fractals, vol. 135, p. 109794, Jun. 2020, doi: 10.1016/j.chaos.2020.109794.
C. McArthur et al., “Evaluating the Effect of COVID-19 Pandemic Lockdown on Long-Term Care Residents’ Mental Health: A Data-Driven Approach in New Brunswick,” Journal of the American Medical Directors Association, vol. 22, no. 1, pp. 187–192, Jan. 2021, doi: 10.1016/j.jamda.2020.10.028.
L. Li et al., “Propagation analysis and prediction of the COVID-19,” Infectious Disease Modelling, vol. 5, pp. 282–292, Jan. 2020, doi: 10.1016/j.idm.2020.03.002.
A.-L. Balogun et al., “Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms,” Geoscience Frontiers, vol. 12, no. 3, p. 101104, May 2021, doi: 10.1016/j.gsf.2020.10.009.
X. Wang, S. Gao, S. Zhou, Y. Guo, Y. Duan, and D. Wu, “Prediction of House Price Index Based on Bagging Integrated WOA-SVR Model,” Mathematical Problems in Engineering, vol. 2021, p. e3744320, Oct. 2021, doi: 10.1155/2021/3744320.
R. Cheng, J. Yu, M. Zhang, C. Feng, and W. Zhang, “Short-term hybrid forecasting model of ice storage air-conditioning based on improved SVR,” Journal of Building Engineering, vol. 50, p. 104194, Jun. 2022, doi: 10.1016/j.jobe.2022.104194.
D. Parbat and M. Chakraborty, “A python based support vector regression model for prediction of COVID19 cases in India,” Chaos Solitons Fractals, vol. 138, p. 109942, Sep. 2020, doi: 10.1016/j.chaos.2020.109942.
N. P. Dharani, P. Bojja, and P. Raja Kumari, “Evaluation of performance of an LR and SVR models to predict COVID-19 pandemic,” Materials Today: Proceedings, Feb. 2021, doi: 10.1016/j.matpr.2021.02.166.
E. Setti et al., “Predicting post COVID-19 rehabilitation duration with linear kernel SVR,” in 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Jul. 2021, pp. 1–5. doi: 10.1109/BHI50953.2021.9508602.
P. R. Warde et al., “Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic,” BMJ Health Care Inform, vol. 28, no. 1, p. e100248, May 2021, doi: 10.1136/bmjhci-2020-100248.
O. Torrealba-Rodriguez, R. A. Conde-Gutiérrez, and A. L. Hernández-Javier, “Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models,” Chaos, Solitons & Fractals, vol. 138, p. 109946, Sep. 2020, doi: 10.1016/j.chaos.2020.109946.
M. E. El Zowalaty and J. D. Järhult, “From SARS to COVID-19: A previously unknown SARS- related coronavirus (SARS-CoV-2) of pandemic potential infecting humans – Call for a One Health approach,” One Health, vol. 9, p. 100124, Jun. 2020, doi: 10.1016/j.onehlt.2020.100124.
C. Gorges, K. Öztürk, and R. Liebich, “Impact detection using a machine learning approach and experimental road roughness classification,” Mechanical Systems and Signal Processing, vol. 117, pp. 738–756, Feb. 2019, doi: 10.1016/j.ymssp.2018.07.043.
L. J. Muhammad, E. A. Algehyne, S. S. Usman, A. Ahmad, C. Chakraborty, and I. A. Mohammed, “Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset,” SN COMPUT. SCI., vol. 2, no. 1, p. 11, Nov. 2020, doi: 10.1007/s42979-020-00394-7.
“Ini 2 Gejala Utama Sub Varian BA.2 alias ‘Son of Omicron.’” https://health.detik.com/berita-detikhealth/d-5938598/ini-2-gejala-utama-sub-varian-ba2-alias-son-of-omicron (accessed Aug. 24, 2022).
M. Rasyid, Z. Zainuddin, and A. Andani, “Early Detection of Health Kindergarten Student at School Using Image Processing Technology,” in Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia, Makassar, Indonesia, 2019. doi: 10.4108/eai.2-5-2019.2284609.
M. F. Rasyid, D. Imran, and A. A. Mahersatillah, “Prediksi penyebaran Sub Varian omicron di Indonesia menggunakan Machine Learning,” SISITI : Seminar Ilmiah Sistem Informasi dan Teknologi Informasi, vol. 11, no. 1, Art. no. 1, Aug. 2022, Accessed: Jan. 11, 2023. [Online]. Available: https://www.ejurnal.dipanegara.ac.id/index.php/sisiti/article/view/936
M. F. Rohmah, I. K. G. D. Putra, R. S. Hartati, and L. Ardiantoro, “Comparison Four Kernels of SVR to Predict Consumer Price Index,” J. Phys.: Conf. Ser., vol. 1737, no. 1, p. 012018, Jan. 2021, doi: 10.1088/1742-6596/1737/1/012018.
M. F. Rasyid, “Comparison Of LBPH, Fisherface, and PCA For Facial Expression Recognition of Kindergarten Student,” International Journal Education and Computer Studies (IJECS), vol. 2, no. 1, Art. no. 1, May 2022, doi: 10.35870/ijecs.v2i1.625.
“Support Vector Regression (SVR) using linear and non-linear kernels,” scikit-learn. https://scikit-learn/stable/auto_examples/svm/plot_svm_regression.html (accessed Aug. 24, 2022).
Ali Hassan Sial, Syed Yahya Shah Rashdi, and Dr. Abdul Hafeez Khan, “Comparative Analysis of Data Visualization Libraries Matplotlib and Seaborn in Python,” IJATCSE, vol. 10, no. 1, pp. 277–281, Feb. 2021, doi: 10.30534/ijatcse/2021/391012021.
R. O. Odegua and F. O. Ikpotokin, “DataSist: A Python-based library for easy data analysis, visualization and modeling,” p. 17.
Downloads
Published
Issue
Section
License
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to CoreIT journal and published by Informatics Engineering Department Universitas Islam Negeri Sultan Syarif Kasim Riau as publisher of the journal.
Authors who publish with this journal agree to the following terms:
Authors automatically transfer the copyright to the journal and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike (CC BY SA) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate permission for non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
