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Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
,
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...
| Published in: | Heliyon |
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| ISSN: | 2405-8440 |
| Published: |
Elsevier BV
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63389 |
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| 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 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. |
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| College: |
Faculty of Science and Engineering |
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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). |
| Issue: |
5 |
| Start Page: |
e16015 |