Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis

Aims: By associating features with orthonormal bases, we analyse the values of the extracted features for the daily biweekly growth rates of COVID-19 confirmed cases and deaths on national and continental levels. Methods: By adopting the concept of Fourier coefficients, we analyse the inner pro...

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Autor principal:	Ray-Ming Chen
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	covid-19 biweekly growth rates variability fourier analysis temporal and spatial Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/8e71aaf21194464fa9798c3562e7db83
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spelling	oai:doaj.org-article:8e71aaf21194464fa9798c3562e7db832021-11-11T01:15:36ZExtracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis10.3934/mbe.20213111551-0018https://doaj.org/article/8e71aaf21194464fa9798c3562e7db832021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021311?viewType=HTMLhttps://doaj.org/toc/1551-0018Aims: By associating features with orthonormal bases, we analyse the values of the extracted features for the daily biweekly growth rates of COVID-19 confirmed cases and deaths on national and continental levels. Methods: By adopting the concept of Fourier coefficients, we analyse the inner products with respect to temporal and spatial frequencies on national and continental levels. The input data are the global time series data with 117 countries over 109 days on a national level; and 6 continents over 447 days on a continental level. Next, we calculate the Euclidean distance matrices and their average variabilities, which measure the average discrepancy between one feature vector and all others. Then we analyse the temporal and spatial variabilities on a national level. By calculating the temporal inner products on a continental level, we derive and analyse the similarities between the continents. Results: On the national level, the daily biweekly growth rates bear higher similarities in the time dimension than the ones in the space dimension. Furthermore, there exists a strong concurrency between the features for biweekly growth rates of cases and deaths. As far as the trends of the features are concerned, the features are stabler on the continental level, and less predictive on the national level. In addition, there are very high similarities between all the continents, except Asia. Conclusions: The features for daily biweekly growth rates of cases and deaths are extracted via orthonormal frequencies. By tracking the inner products for the input data and the orthonormal features, we could decompose the evolutionary results of COVID-19 into some fundamental frequencies. Though the frequency-based techniques are applied, the interpretation of the features should resort to other methods. By analysing the spectrum of the frequencies, we reveal hidden patterns of the COVID-19 pandemic. This would provide some preliminary research merits for further insightful investigations. It could also be used to predict future trends of daily biweekly growth rates of COVID-19 cases and deaths.Ray-Ming ChenAIMS Pressarticlecovid-19biweekly growth ratesvariabilityfourier analysistemporal and spatialBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6216-6238 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 biweekly growth rates variability fourier analysis temporal and spatial Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	covid-19 biweekly growth rates variability fourier analysis temporal and spatial Biotechnology TP248.13-248.65 Mathematics QA1-939 Ray-Ming Chen Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis
description	Aims: By associating features with orthonormal bases, we analyse the values of the extracted features for the daily biweekly growth rates of COVID-19 confirmed cases and deaths on national and continental levels. Methods: By adopting the concept of Fourier coefficients, we analyse the inner products with respect to temporal and spatial frequencies on national and continental levels. The input data are the global time series data with 117 countries over 109 days on a national level; and 6 continents over 447 days on a continental level. Next, we calculate the Euclidean distance matrices and their average variabilities, which measure the average discrepancy between one feature vector and all others. Then we analyse the temporal and spatial variabilities on a national level. By calculating the temporal inner products on a continental level, we derive and analyse the similarities between the continents. Results: On the national level, the daily biweekly growth rates bear higher similarities in the time dimension than the ones in the space dimension. Furthermore, there exists a strong concurrency between the features for biweekly growth rates of cases and deaths. As far as the trends of the features are concerned, the features are stabler on the continental level, and less predictive on the national level. In addition, there are very high similarities between all the continents, except Asia. Conclusions: The features for daily biweekly growth rates of cases and deaths are extracted via orthonormal frequencies. By tracking the inner products for the input data and the orthonormal features, we could decompose the evolutionary results of COVID-19 into some fundamental frequencies. Though the frequency-based techniques are applied, the interpretation of the features should resort to other methods. By analysing the spectrum of the frequencies, we reveal hidden patterns of the COVID-19 pandemic. This would provide some preliminary research merits for further insightful investigations. It could also be used to predict future trends of daily biweekly growth rates of COVID-19 cases and deaths.
format	article
author	Ray-Ming Chen
author_facet	Ray-Ming Chen
author_sort	Ray-Ming Chen
title	Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis
title_short	Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis
title_full	Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis
title_fullStr	Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis
title_full_unstemmed	Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis
title_sort	extracted features of national and continental daily biweekly growth rates of confirmed covid-19 cases and deaths via fourier analysis
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/8e71aaf21194464fa9798c3562e7db83
work_keys_str_mv	AT raymingchen extractedfeaturesofnationalandcontinentaldailybiweeklygrowthratesofconfirmedcovid19casesanddeathsviafourieranalysis
_version_	1718439603684245504

Extracted features of national and continental daily biweekly growth rates of confirmed COVID-19 cases and deaths via Fourier analysis

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