[1] WHO. WHO director-general’s opening remarks at the media briefing on COVID-19 - 11 March 2020. World Health Organization. 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020. [2020-3-11].
[2] Liang JY, Liu RB, He W, Zeng ZQ, Wang YQX, Wang BY, et al. Infection rates of 70% of the population observed within 3 weeks after release of COVID-19 restrictions in Macao, China. J Infect 2023;86(4):402 − 4.CrossRef
[3] Zeng ZQ, Qu W, Liu RB, Guan WD, Liang JY, Lin ZJ, et al. Real-time assessment of COVID-19 epidemic in Guangdong Province, China using mathematical models. J Thorac Dis 2023;15(3):1517 − 22.CrossRef
[4] Zhou MZ, Kan MY. The varying impacts of COVID-19 and its related measures in the UK: A year in review. PLoS One 2021;16(9):e0257286.CrossRef
[5] Elliott P, Bodinier B, Eales O, Wang HW, Haw D, Elliott J, et al. Rapid increase in omicron infections in england during december 2021: REACT-1 study. Science 2022;375(6587):1406 − 11.CrossRef
[6] Kirsebom FCM, Andrews N, Stowe J, Toffa S, Sachdeva R, Gallagher E, et al. COVID-19 vaccine effectiveness against the omicron (BA. 2) variant in England. Lancet Infect Dis 2022;22(7):931 − 3.CrossRef
[7] Okabe Y, Shudo A. Microscopic numerical simulations of epidemic models on networks. Mathematics 2021;9(9):932.CrossRef
[8] Huang AQ, Liu XJ, Rao CR, Zhang Y, He YF. A new container throughput forecasting paradigm under COVID-19. Sustainability, 2022;14(5):2990.CrossRef
[9] Dong R, Ni SW, Ikuno S. Nonlinear frequency analysis of COVID-19 spread in Tokyo using empirical mode decomposition. Sci Rep 2022;12(1):2175.CrossRef
[10] Saâdaoui F, Mefteh-Wali S, Ben Jabeur S. Multiresolutional statistical machine learning for testing interdependence of power markets: a Variational Mode Decomposition-based approach. Expert Syst Appl 2022;208:118161.CrossRef
[11] Qu W, Chui CK, Deng GT, Qian T. Sparse representation of approximation to identity. Anal Appl 2022;20(4):815 − 37.CrossRef
[12] Qian T, Zhang Y, Liu WQ, Qu W. Adaptive Fourier decomposition-type sparse representations versus the Karhunen-Loève expansion for decomposing stochastic processes. Math Methods Appl Sci 2023;46(13):14007 − 25.CrossRef
[13] Lu GB, Yang ZF, Qu W, Qian T, Liu ZG, He W, et al. Daily fluctuations in COVID-19 infection rates under Tokyo's epidemic prevention measures–new evidence from adaptive Fourier decomposition. Front Public Health 2023;11:1245572.CrossRef
[14] Qian T, Zhang LM, Li ZX. Algorithm of adaptive fourier decomposition. IEEE Trans Signal Process, 2011;59(12):5899 − 906.CrossRef
[15] Qian T, Wang YB. Adaptive Fourier series – a variation of greedy algorithm. Adv Comput Math 2011;34(3):279 − 93.CrossRef
[16] Qian T, Wang JZ, Mai WX. An enhancement algorithm for cyclic adaptive fourier decomposition. Appl Comput Harmon Anal 2019;47(2):516 − 25.CrossRef
[17] Qian T. Two-dimensional adaptive fourier decomposition. Math Methods Appl Sci 2016;39(10):2431 − 48.CrossRef
[18] Qian T. Sparse representations of random signals. Math Methods Appl Sci 2022;45(8):4210 − 30.CrossRef
[19] Hon C, Liu ZG, Qian T, Qu W, Zhao JM. Trends by adaptive fourier decomposition and application in prediction. Int J Wavelets Multiresolut Inf Process. http://dx.doi.org/10.1142/S0219691324500140.
[20] Qu W, Qian T, Deng GT. A stochastic sparse representation: n-best approximation to random signals and computation. Appl Comput Harmon Anal 2021;55:185 − 98.CrossRef
[21] Pearson K. VII. Note on regression and inheritance in the case of two parents. Proc Roy Soc London 1895;58(347-352):240 − 2.CrossRef
[22] Tartof SY, Slezak JM, Fischer H, Hong V, Ackerson BK, Ranasinghe ON, et al. Effectiveness of mRNA BNT162b2 COVID-19 vaccine up to 6 months in a large integrated health system in the USA: a retrospective cohort study. Lancet 2021;398(10309):1407 − 16.CrossRef
[23] Ghosh A, Nundy S, Ghosh S, Mallick TK. Study of COVID-19 pandemic in London (UK) from urban context. Cities 2020;106:102928.CrossRef
[24] Jarvis CI, Van Zandvoort K, Gimma A, Prem K, CMMID COVID-19 working group, Klepac P, et al. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Med 2020;18(1):124.CrossRef