COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis

Abstract Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. At the time of writing, COVID-19 time series for many countries are short: pre- and post-lockdown series are shorter still. Accurate estima...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Guy P. Nason
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/09757d515dad453b90f92eb8d7923c17
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:09757d515dad453b90f92eb8d7923c17
record_format	dspace
spelling	oai:doaj.org-article:09757d515dad453b90f92eb8d7923c172021-12-02T13:34:10ZCOVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis10.1038/s41598-020-79092-62045-2322https://doaj.org/article/09757d515dad453b90f92eb8d7923c172020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79092-6https://doaj.org/toc/2045-2322Abstract Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. At the time of writing, COVID-19 time series for many countries are short: pre- and post-lockdown series are shorter still. Accurate estimation of potentially interesting cycles seems beyond reach with such short series. We solve the problem of obtaining accurate estimates from short series by using recent Bayesian spectral fusion methods. We show that transformed daily COVID-19 cases for many countries generally contain three cycles operating at wavelengths of around 2.7, 4.1 and 6.7 days (weekly) and that shorter wavelength cycles are suppressed after lockdown. The pre- and post-lockdown differences suggest that the weekly effect is at least partly due to non-epidemic factors. Unconstrained, new cases grow exponentially, but the internal cyclic structure causes periodic declines. This suggests that lockdown success might only be indicated by four or more daily falls. Spectral learning for epidemic time series contributes to the understanding of the epidemic process and can help evaluate interventions. Spectral fusion is a general technique that can fuse spectra recorded at different sampling rates, which can be applied to a wide range of time series from many disciplines.Guy P. NasonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guy P. Nason COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis
description	Abstract Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. At the time of writing, COVID-19 time series for many countries are short: pre- and post-lockdown series are shorter still. Accurate estimation of potentially interesting cycles seems beyond reach with such short series. We solve the problem of obtaining accurate estimates from short series by using recent Bayesian spectral fusion methods. We show that transformed daily COVID-19 cases for many countries generally contain three cycles operating at wavelengths of around 2.7, 4.1 and 6.7 days (weekly) and that shorter wavelength cycles are suppressed after lockdown. The pre- and post-lockdown differences suggest that the weekly effect is at least partly due to non-epidemic factors. Unconstrained, new cases grow exponentially, but the internal cyclic structure causes periodic declines. This suggests that lockdown success might only be indicated by four or more daily falls. Spectral learning for epidemic time series contributes to the understanding of the epidemic process and can help evaluate interventions. Spectral fusion is a general technique that can fuse spectra recorded at different sampling rates, which can be applied to a wide range of time series from many disciplines.
format	article
author	Guy P. Nason
author_facet	Guy P. Nason
author_sort	Guy P. Nason
title	COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis
title_short	COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis
title_full	COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis
title_fullStr	COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis
title_full_unstemmed	COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis
title_sort	covid-19 cycles and rapidly evaluating lockdown strategies using spectral analysis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/09757d515dad453b90f92eb8d7923c17
work_keys_str_mv	AT guypnason covid19cyclesandrapidlyevaluatinglockdownstrategiesusingspectralanalysis
_version_	1718392777349267456

COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis

Ejemplares similares