Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic

Harvey, John; Chan, Bryan; Srivastava, Tarun; Zarebski, Alexander E.; Dlotko, Pawel; Błaszczyk, Piotr; Parkinson, Rachel H.; White, Lisa J.; Aguas, Ricardo; Mahdi, Adam

doi:10.1016/j.heliyon.2023.e16015

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Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic

John Harvey

, Bryan Chan, Tarun Srivastava, Alexander E. Zarebski, Pawel Dlotko

, Piotr Błaszczyk, Rachel H. Parkinson

, Lisa J. White

, Ricardo Aguas, Adam Mahdi

Heliyon, Volume: 9, Issue: 5, Start page: e16015

Swansea University Authors: John Harvey , Pawel Dlotko

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DOI (Published version): 10.1016/j.heliyon.2023.e16015

Abstract

IntroductionA discussion of ‘waves’ of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only te...

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Published in:	Heliyon
ISSN:	2405-8440
Published:	Elsevier BV 2023
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URI:	https://cronfa.swan.ac.uk/Record/cronfa63389
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This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves.ResultsThe output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves.ConclusionIt is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.</abstract><type>Journal Article</type><journal>Heliyon</journal><volume>9</volume><journalNumber>5</journalNumber><paginationStart>e16015</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2405-8440</issnPrint><issnElectronic/><keywords/><publishedDay>1</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-05-01</publishedDate><doi>10.1016/j.heliyon.2023.e16015</doi><url>http://dx.doi.org/10.1016/j.heliyon.2023.e16015</url><notes/><college>COLLEGE NANME</college><department>Mathematics</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SMA</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>his work was supported by UK Research & Innovation [grant number EP/W012294/1]. JH was further supported by a UK Research & Innovation Future Leaders Fellowship [grant number MR/W01176X/1] as well as by a Daphne Jackson Fellowship, sponsored by Swansea University and the UK Engineering and Physical Sciences Research Council. AEZ is supported by The Oxford Martin Programme on Pandemic Genomics. PD acknowledges support of the Dioscuri program initiated by the Max Planck Society, jointly managed with the National Science Centre (Poland), and mutually funded by the Polish Ministry of Science and Higher Education and the German Federal Ministry of Education and Research. 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spelling	v2 63389 2023-05-10 Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic 1a837434ec48367a7ffb596d04690bfd 0000-0001-9211-0060 John Harvey John Harvey true false 403ec9c6f5967333948eabebe06a75f5 0000-0001-5352-3102 Pawel Dlotko Pawel Dlotko true false 2023-05-10 SMA IntroductionA discussion of ‘waves’ of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous.MethodsWe present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as ‘observed waves’. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves.ResultsThe output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves.ConclusionIt is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic. Journal Article Heliyon 9 5 e16015 Elsevier BV 2405-8440 1 5 2023 2023-05-01 10.1016/j.heliyon.2023.e16015 http://dx.doi.org/10.1016/j.heliyon.2023.e16015 COLLEGE NANME Mathematics COLLEGE CODE SMA Swansea University his work was supported by UK Research & Innovation [grant number EP/W012294/1]. JH was further supported by a UK Research & Innovation Future Leaders Fellowship [grant number MR/W01176X/1] as well as by a Daphne Jackson Fellowship, sponsored by Swansea University and the UK Engineering and Physical Sciences Research Council. AEZ is supported by The Oxford Martin Programme on Pandemic Genomics. PD acknowledges support of the Dioscuri program initiated by the Max Planck Society, jointly managed with the National Science Centre (Poland), and mutually funded by the Polish Ministry of Science and Higher Education and the German Federal Ministry of Education and Research. RA is funded by the Bill and Melinda Gates Foundation (OPP1193472). 2023-06-08T15:29:23.0482046 2023-05-10T10:52:51.6097908 Faculty of Science and Engineering School of Mathematics and Computer Science - Mathematics John Harvey 0000-0001-9211-0060 1 Bryan Chan 2 Tarun Srivastava 3 Alexander E. Zarebski 4 Pawel Dlotko 0000-0001-5352-3102 5 Piotr Błaszczyk 6 Rachel H. Parkinson 0000-0002-8192-3178 7 Lisa J. White 0000-0002-6523-185x 8 Ricardo Aguas 9 Adam Mahdi 10 63389__27562__a41c2a53b1bb4a38bd5cc8527d79cc1d.pdf 63389.pdf 2023-05-22T14:49:39.0532859 Output 4008480 application/pdf Version of Record true © 2023 The Authors. Published by Elsevier Ltd. Creative Commons Attribution (CC BY 4.0) true eng http://creativecommons.org/licenses/by/4.0/
title	Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
spellingShingle	Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic John Harvey Pawel Dlotko
title_short	Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
title_full	Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
title_fullStr	Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
title_full_unstemmed	Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
title_sort	Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
author_id_str_mv	1a837434ec48367a7ffb596d04690bfd 403ec9c6f5967333948eabebe06a75f5
author_id_fullname_str_mv	1a837434ec48367a7ffb596d04690bfd_*_John Harvey 403ec9c6f5967333948eabebe06a75f5_*_Pawel Dlotko
author	John Harvey Pawel Dlotko
author2	John Harvey Bryan Chan Tarun Srivastava Alexander E. Zarebski Pawel Dlotko Piotr Błaszczyk Rachel H. Parkinson Lisa J. White Ricardo Aguas Adam Mahdi
format	Journal article
container_title	Heliyon
container_volume	9
container_issue	5
container_start_page	e16015
publishDate	2023
institution	Swansea University
issn	2405-8440
doi_str_mv	10.1016/j.heliyon.2023.e16015
publisher	Elsevier BV
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department_str	School of Mathematics and Computer Science - Mathematics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Mathematics
url	http://dx.doi.org/10.1016/j.heliyon.2023.e16015
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description	IntroductionA discussion of ‘waves’ of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous.MethodsWe present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as ‘observed waves’. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves.ResultsThe output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves.ConclusionIt is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.
published_date	2023-05-01T15:29:21Z
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Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic

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