Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic
© 2023 The Authors..
Introduction: A 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.
Methods: We 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.
Results: The 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.
Conclusion: It 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Heliyon - 9(2023), 5 vom: 16. Mai, Seite e16015 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Harvey, John [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Revised 20.09.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.heliyon.2023.e16015 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM356997162 |
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| 500 | |a Date Revised 20.09.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 The Authors. | ||
| 520 | |a Introduction: A 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 | ||
| 520 | |a Methods: We 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 | ||
| 520 | |a Results: The 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 | ||
| 520 | |a Conclusion: It 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Case fatality ratio | |
| 650 | 4 | |a Epidemic wave | |
| 650 | 4 | |a Non-pharmaceutical intervention | |
| 650 | 4 | |a Time series | |
| 700 | 1 | |a Chan, Bryan |e verfasserin |4 aut | |
| 700 | 1 | |a Srivastava, Tarun |e verfasserin |4 aut | |
| 700 | 1 | |a Zarebski, Alexander E |e verfasserin |4 aut | |
| 700 | 1 | |a Dłotko, Paweł |e verfasserin |4 aut | |
| 700 | 1 | |a Błaszczyk, Piotr |e verfasserin |4 aut | |
| 700 | 1 | |a Parkinson, Rachel H |e verfasserin |4 aut | |
| 700 | 1 | |a White, Lisa J |e verfasserin |4 aut | |
| 700 | 1 | |a Aguas, Ricardo |e verfasserin |4 aut | |
| 700 | 1 | |a Mahdi, Adam |e verfasserin |4 aut | |
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