AN ESTIMATION OF NUMBER OF DAILY DEATHS DUE TO COVID-19 IN UNITED STATES OF AMERICA, UNITED KINGDOM AND TURKEY
DOI:
https://doi.org/10.11113/aej.v13.17713Keywords:
Covid-19, Kernel, Pandemic spread, Modelling, Support vector regression.Abstract
Covid-19 virus is threatening the world with health, social and economic implications and all around the world data is obtained continuously with pandemic for modelling and predicting the future. In this work, support vector regression technique was used to make some predictions on the daily death values due to Covid-19 virus. The models were created for the world, United States of America, United Kingdom and Turkey. All the regression models were tested using coefficient of determination (R2) and root mean square error (RMSE) values. The analysis was also conducted for comparing the suitability of linear, radial and polynomial kernels. The radial kernel produced relatively better results. In predicting the world data support vector regression with radial kernel produced 0.805262 R2 value on test data. In the models created for United States of America 0.723376 R2 value, for United Kingdom 0.95412 R2 value and for Turkey 0.875343 R2 value using test data were observed. Also, while the models were created for specific countries the comparisons were made between using only data for the country and also using the whole world data. In general modelling using the data for the world combined with the country data gave better prediction.
References
Vespignani A., Tian H., Dye C., Lloyd-Smith J. O., Eggo R. M., Shrestha M., Scarpino S. V., Gutierrez B., Kraemer M. U. G., Wu J., Leung K. and Leung G. M. 2020. Modelling COVID-19. Nature Reviews Physics, 2020. 279-281. DOI: https://doi.org/10.1038/s42254-020-0178-4
Arino J., and Portet S. 2020. Simple Model for COVID-19. Infectious Disease Modelling. 5: 309-315. DOI: https://doi.org/10.1016/j.idm.2020.04.002
Abdo M. S., Shah K. Wahash H. A. and Panchal S. K. 2020. On a comprehensive model of the novel coronavirus (COVID-19) under Mittag-Leffler derivative. Chaos, Solitons, Fractals. 135,109867 2020. DOI: https://doi.org/10.1016/j.chaos.2020.109867
Sajadi M. M., Habibzadeh P. Vintzileos A., Shokouhi S., Wilhelm F. M., and Amoroso A. 2020. Temperature, humidity, and latitude analysis t predict potential spread and seasonality for COVID-19. SSRN 2020. 3550308. DOI: https://doi.org/10.2139/ssrn.3550308
Yang Z., Zeng Z., Wang K., Wong S., Liang W. Zanin M., … He J. 2020. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease. 12(3): 165-174. DOI: https://doi.org/10.21037/jtd.2020.02.64
Fanelli D. and Piazza F. 2020. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons, Fractals. 134, 109761, 2020. DOI: https://doi.org/10.1016/j.chaos.2020.109761
Djilali S. and Ghanbari B. 2020. Coronavirus pandemic: A predictive analysis of the peak outbreak epidemic in South Africa, Turkey and Brazil. Chaos, Solitons, Fractals. 138, 109971, 2020. DOI: https://doi.org/10.1016/j.chaos.2020.109971
Vapnik V. and Lerner A. 1963. Pattern recognition using generalized portrait method. Automation and Remote Control. 24: 774-780.
Smola A. J., Scholkopf B. 2004. A Tutorial on Support Vector Regression. Statistics and Computing. 14: 199-222. DOI: https://doi.org/10.1023/B:STCO.0000035301.49549.88
Basak D., Pal S. and Patranabis D. C. 2007. Support Vector Regression. Neural Information Processing –Letters and Reviews. 11(10): 203-224.
Boughorbel S., Tarel J. P., Boujema N. 2005. Conditionally positive definite kernels for SVM based image recognition. IEEE International Conference on Multimedia and Expo, July 2005. DOI: https://doi.org/10.1109/ICME.2005.1521373
Zhou D. X. and Jetter K. 2006. Approximation with polynomial kernels and SVM classifiers. Advances in Computational Mathematics. 25:323-344. DOI: https://doi.org/10.1007/s10444-004-7206-2
Guo B. Gunn S. R. Damper R. I. and Nelson J.D.B. 2008. Customizing kernel functions for SVM-Based Hyperspectral Image Classification. IEEE Transactions on Image Processing. 17(4):622-629. DOI: https://doi.org/10.1109/TIP.2008.91895
Ulker E.D. and Ulker S. 2019. Unemployment rate and GDP prediction using support vector regression. Proceedings of the 1st International Conference on Advanced Information Science and System November 2019. 17:1-5. DOI: https://doi.org/10.1145/3373477.3373494
Zhong H., Wang J., Jia H., Mu Y., and LV S. 2019. Vector field-based support vector regression for building energy consumption prediction. Applied Energy. 242:403-414. DOI: https://doi.org/10.1016/j.apenergy.2019.2019.03.078
Nourali H. and Osanloo M. 2018. Mining capital cost estimation using support vector regression (SVR). Resource Policy. 62:527-540. DOI: https://doi.org/10.1016/j.resourpol.2018.10.008
Zhao D., Liu H., Zheng Y., He Y., Lu D. and Lyu C. 2019. A reliable method for colorectal cancer prediction based on feature selection and support vector machine. Medical & Biological Engineering & Computing. 57:901-912. DOI: https://doi.org/10.1007/s11517-018-1930-0
Nguyen H. 2019. Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam. SN Applied Sciences. 1, 283 2019. DOI: https://doi.org/10.1007/s42452-019-0295-9
Tan Z., Zhang J., He Y., Zhang Y., Xiong G. and Liu Y. 2020. Short-Term Load Forecasting Based on Integration of SVR and Stacking. IEEE Access. 8: 227719-227728, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3041779
Liu Q., Liu W., Mei J., Si G., Xia T. and Quan J. 2021. A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application. Shock and Vibration. 6675078, 2021. DOI: https://doi.org/10.1155/2021/6675078
Coronavirus statistics reports. 2020. http://www.virusncov.com [Accessed September 16, 2020].
