COVID-19 cycles and rapidly evaluating lockdown strategies using spectral analysis.
File(s)
Published version
Author(s)
Nason, Guy
Type
Journal Article
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. Weshow 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.
Date Issued
2020-12-17
Date Acceptance
2020-11-30
Citation
Scientific Reports, 2020, 10 (22134), pp.1-12
ISSN
2045-2322
Publisher
Nature Publishing Group
Start Page
1
End Page
12
Journal / Book Title
Scientific Reports
Volume
10
Issue
22134
Copyright Statement
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons licence, and indicate if changes were made. Te images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons licence, and indicate if changes were made. Te images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.nature.com/articles/s41598-020-79092-6
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
MEASLES
Bayes Theorem
COVID-19
Communicable Disease Control
Humans
Quarantine
SARS-CoV-2
Social Isolation
Humans
Bayes Theorem
Social Isolation
Communicable Disease Control
Quarantine
COVID-19
SARS-CoV-2
Publication Status
Published
Date Publish Online
2020-12-17