Keywords and phrases: wavelet transform, time-series data, epidemic, SVM, kernel trick.
Received: June 26, 2022; Revised: January 17, 2023; Accepted: January 24, 2023; Published: April 20, 2023
How to cite this article: Shivshanker Singh Patel, Wavelet transform and kernel trick to model spread of an epidemic, Advances and Applications in Statistics 87(1) (2023), 61-79. http://dx.doi.org/10.17654/0972361723029
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] D. Fanelli and F. Piazza, Analysis and forecast of covid-19 spreading in China, Italy and France, Chaos Solitons Fractals 134 (2020), Article: 109761. doi: 10.1016/j.chaos.2020.109761. [2] J. T. Wu, K. Leung and G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-ncov outbreak originating in Wuhan, China: a modelling study, The Lancet 395 (2020), 689-697. doi: 10.1016/S0140-6736(20)30260-9. [3] Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, R. Ren, K. S. Leung, E. H. Lau, J. Y. Wong, X. Xing, N. Xiang, Y. Wu, C. Li, Q. Chen, D. Li, T. Liu, J. Zhao, M. Liu, W. Tu, C. Chen, L. Jin, R. Yang, Q. Wang, S. Zhou, R. Wang, H. Liu, Y. Luo, Y. Liu, G. Shao, H. Li, Z. Tao, Y. Yang, Z. Deng, B. Liu, Z. Ma, Y. Zhang, G. Shi, T. T. Lam, J. T. Wu, G. F. Gao, B. J. Cowling, B. Yang, G. M. Leung and Z. Feng, Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, New England Journal of Medicine 382 (2020), 1199-1207. doi: 10.1056/nejmoa2001316. [4] S. Zhao, Q. Lin, J. Ran, S. S. Musa, G. Yang, W. Wang, Y. Lou, D. Gao, L. Yang, D. He and M. H. Wang, Preliminary estimation of the basic reproduction number of novel coronavirus (2019-ncov) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak, International Journal of Infectious Diseases 92 (2020), 214-217. doi: 10.1016/j.ijid.2020.01.050. [5] C. Anastassopoulou, L. Russo, A. Tsakris and C. Siettos, Data-based analysis, modelling and forecasting of the Covid-19 outbreak, PLoS ONE 15(3) (202), e0230405. doi: 10.1371/journal.pone.0230405. [6] A. Ghatak, S. Singh Patel, S. Bonnerjee and S. Roy, A generalized epidemiological model with dynamic and asymptomatic population, Statistical Methods in Medical Research 31(11) (2022), 2137-2163. doi:10.1177/09622802221115877. [7] K. Roosa, Y. Lee, R. Luo, A. Kirpich, R. Rothenberg, J. M. Hyman, P. Yan and G. Chowell, Real-time forecasts of the covid-19 epidemic in China from February 5th to February 24th, 2020, Infectious Disease Modelling 5 (2020), 256-263. doi: 10.1016/j.idm.2020.02.002. [8] A. Ahmadi, Y. Fadaei, M. Shirani and F. Rahmani, Modeling and forecasting trend of COVID-19 epidemic in Iran until May 13, 2020, Med. J. Islam Repub. Iran 34 (2020), 27. doi: 10.34171/mjiri.34.27. PMID: 32617266; PMCID: PMC7320984. [9] F. Petropoulos and S. Makridakis, Forecasting the novel coronavirus Covid-19, PLoS ONE 15(3) (2020), e0231236. doi: 10.1371/journal.pone.0231236. [10] Z. Hu, Q. Ge, S. Li and M. Xiong, Artificial intelligence forecasting of Covid-19 in China, International Journal of Educational Excellence 6 (2020), 71-94. doi: 10.18562/ijee.054. [11] M. A. Al-Qaness, A. A. Ewees, H. Fan and M. A. E. Aziz, Optimization method for forecasting confirmed cases of Covid-19 in China, Journal of Clinical Medicine 9 (2020), 674. doi: 10.3390/jcm9030674. [12] G. E. Box, G. M. Jenkins and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 4th ed., Wiley, 2013. doi: 10.1002/9781118619193. [13] S. G. Krantz, P. Polyakov and A. S. Rao, True epidemic growth construction through harmonic analysis, Journal of Theoretical Biology 494 (2020), Article 110243. doi: 10.1016/j.jtbi.2020.110243. [14] B. B. Hazarika and D. Gupta, Modelling and forecasting of Covid-19 spread using wavelet-coupled random vector functional link networks, Applied Soft Computing Journal 96 (2020), 106626. doi: 10.1016/j.asoc.2020.106626. [15] A. Kaya, F. Cemrek and O. Ozdemir, Forecasting analysis of Covid-19 cases with wavelet neural network and time series approach, International Journal of Neural Networks and Advanced Applications 8 (2021), 6-11. doi: 10.46300/91016.2021.8.2. [16] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning 20 (1995), 273-297. doi: 10.1023/A:1022627411411. [17] J. Mercer, Functions of positive and negative type, and their connection the theory of integral equations, Philosophical Transactions of the Royal Society of London, Series A, Containing Papers of a Mathematical or Physical Character 209 (1909), 415-446. doi: 10.1098/rsta.1909.0016. [18] A. J. Smola, B. Schölkopf and K. R. Müller, The connection between regularization operators and support vector kernels, Neural Networks 11 (1998), 637-649. doi: 10.1016/S0893-6080(98)00032-X. [19] I. Daubechies, the wavelet transform, time-frequency localization and signal analysis, IEEE Transactions on Information Theory 36 (1990), 961-1005. doi: 10.1109/18.57199. [20] F. Wu and Y. Zhao, Least squares support vector machine on Morlet wavelet kernel function and its application to nonlinear system identification, Information Technology Journal 5 (2006), 439-444. doi: 10.3923/itj.2006.439.444. [21] Nalini Chintalapudi, Gopi Battineni and Francesco Amenta, COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: a data driven model approach, Journal of Microbiology, Immunology and Infection 53(3) (2020), 396-403.
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