Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points
Stockholm University, Faculty of Science, Department of Mathematics.ORCID iD: 0000-0002-0423-6702
Number of Authors: 42021 (English)In: Epidemiology and Infection, ISSN 0950-2688, E-ISSN 1469-4409, Vol. 149, p. 1-7, article id e68Article in journal (Refereed) Published
Abstract [en]

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

Place, publisher, year, edition, pages
2021. Vol. 149, p. 1-7, article id e68
Keywords [en]
Change point, COVID-19, epidemiology
National Category
Public Health, Global Health and Social Medicine
Identifiers
URN: urn:nbn:se:su:diva-193218DOI: 10.1017/S0950268821000558ISI: 000629562500001PubMedID: 33691815OAI: oai:DiVA.org:su-193218DiVA, id: diva2:1555095
Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2025-02-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed

Authority records

Günther, FelixHöhle, MichaelBender, Andreas

Search in DiVA

By author/editor
Günther, FelixHöhle, MichaelBender, Andreas
By organisation
Department of Mathematics
In the same journal
Epidemiology and Infection
Public Health, Global Health and Social Medicine

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 46 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf