Pattern analysis of continuous analytic wavelet transforms of the COVID19 spreading and death

Yanshuo Wang; Yanshuo Wang

doi:10.3934/bdia.2020003

Big Data and Information Analytics

2020, Volume 5, Issue 1: 29-46. doi: 10.3934/bdia.2020003

Previous Article Next Article

Research article

Pattern analysis of continuous analytic wavelet transforms of the COVID19 spreading and death

Yanshuo Wang ^,

Data Mining and Reliability Consultant, LLLW LLC Inc., Lansing, MI, USA

Received: 15 August 2020 Accepted: 24 September 2020 Published: 09 October 2020

This paper is to use the wavelet transform method with Morse wavelet to analyze the daily confirmed cases and deaths of COVID19 in China, U.S.A, Spain, Italy, France, Germany, U.K., South Korea, Michigan and New York City. Wavelet transform is frequently used for time series data to extract the frequency localization information in last two decades. The wavelet transform with Morse wavelet is applied to the daily infected cases and deaths of COVID19 at different countries/places. There are multiple scales (frequencies) of COVID19 spreading with different magnitude at the specific time which can be identified in Wavelet magnitude scalogram plots. For example, in China, the highest magnitude (2000 to 2500) spreading happened on 2/12/2020 with scale around 329 to 434, but in U.S.A the highest magnitude (4000 to 4500) spreading happened during 4/25/2020 and 4/28/2020 with scale 137 to 183. The summary of the wavelet magnitude and scale at specific period for different countries/places is presented in this paper.
- COVID19,
- wavelet transform,
- morse wavelet,
- scales (frequencies),
- magnitude,
- localization,
- spreading,
- death
Citation: Yanshuo Wang. Pattern analysis of continuous analytic wavelet transforms of the COVID19 spreading and death[J]. Big Data and Information Analytics, 2020, 5(1): 29-46. doi: 10.3934/bdia.2020003

Related Papers:

Abstract

This paper is to use the wavelet transform method with Morse wavelet to analyze the daily confirmed cases and deaths of COVID19 in China, U.S.A, Spain, Italy, France, Germany, U.K., South Korea, Michigan and New York City. Wavelet transform is frequently used for time series data to extract the frequency localization information in last two decades. The wavelet transform with Morse wavelet is applied to the daily infected cases and deaths of COVID19 at different countries/places. There are multiple scales (frequencies) of COVID19 spreading with different magnitude at the specific time which can be identified in Wavelet magnitude scalogram plots. For example, in China, the highest magnitude (2000 to 2500) spreading happened on 2/12/2020 with scale around 329 to 434, but in U.S.A the highest magnitude (4000 to 4500) spreading happened during 4/25/2020 and 4/28/2020 with scale 137 to 183. The summary of the wavelet magnitude and scale at specific period for different countries/places is presented in this paper.

References

[1]	WorldOMeters. Available from: https://www.worldometers.info/coronavirus/country/us/.
[2]	Click On Detroit News. Available from: https://www.clickondetroit.com/news/local/2020/03/20/michigan-covid-19-data-tracking-case-count-cases-by-county-deaths-cases-by-age-tests/.
[3]	Cazelles B, Cazelles K and Chavez M, (2014) Wavelet analysis in ecology and epidemiology: impact of statistical tests. J. R. Soc. Interface 11: 20130585.
[4]	Grenfell BT, Bjø rnstad ON and Kappey J, (2001) Travelling waves and spatial hierarchies in measles epidemics. Nature 414: 716-723.
[5]	Mathlab wavelet help tutorial. MathWorks.
[6]	Olhede SC and Walden AT, (2002) Generalized morse wavelets. IEEE Trans. Signal Proc. 50: 2661-2670.
[7]	Lilly JM and Olhede SC, (2009) Higher-order properties of analytic wavelets. IEEE Trans. Signal Proc. 57: 146-160.
[8]	Lilly JM and Olhede SC, (2010) On the analytic wavelet transform. IEEE Trans. Infor. Theory 56: 4135-4156.
[9]	Lilly JM and Olhede SC, Generalized Morse wavelets as a superfamily of analytic wavelets. IEEE Trans. Signal Proc. 60: 6036-6041.
[10]	Lilly JM, (2016) jLab: A data analysis package for Matlab, version 1.6.2. Available from: http://www.jmlilly.net/jmlsoft.html.
[11]	Lilly JM, (2017) Element analysis: a wavelet-based method for analysing time-localized events in noisy time series. Proc. R. Soc. A 473: 20160776.
[12]	Wachowiak MP, Wachowiak-Smolíková R, Johnson MJ, et al. (2018) Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography. Philos. Trans. A Math. Phys. Eng. Sci. 376: 20170250.
[13]	Wang Y, (1997) Coupling of Small Scale Turbulence Near the Ground to Large Eddy Structures in a Desert Boundary-Layer. Master of Science thesis, University of Connecticut.
[14]	Miller DR, Wang Y and Cionco R, (1997) Ground Level Gust Frequency Coupled to PBL Large Eddies, in Proceeding of 12th Symposium on Boundary Layers and Turbulence. University of British Columbia, Vancouver, BC, Canada.
[15]	Miller DR, Wang Y and Cionco R, (1997) Detecting of PBL Forcing of Ground Level Winds in a Desert Using Wavelet Analysis Methods, in DTIC ADA344056: Proceedings of the 1997 Battlespace Atmospherics Conference.
[16]	Mallat S, (1989) A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11: 674-693.
[17]	New York City Gov.. Available from: https://www1.nyc.gov/site/doh/covid/covid-19-data.page.
[18]	Wang Y, (2020) Use NHPP (Non-Homogenous Poisson Process) Crow-AMSAA method to model the spreading and death rate of the COVID 19. Presented in the SCIENTIFIC PERSPECTIVESON COVID-19 Conference on May 4th, 2020, Scientific Online Conference.
[19]	Wang Y, (2020) Predict new cases of the coronavirus 19; in Michigan, U.S.A. or other countries using Crow-AMSAA method. Infect. Dis. Model. 5: 459-477.

Reader Comments

Your name:*

Email:*
© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)