Download PDF
CC BY-NC-ND 4.0 · Dtsch Med Wochenschr 2021; 146(01): e1-e9
DOI: 10.1055/a-1286-0212
Originalarbeit

Die frühe Phase der COVID-19-Pandemie in Bayern

The early phase of the COVID-19 pandemic in Bavaria, Germany
Matthias Wjst
1   Institut für Lungenbiologie (iLBD), Helmholtz-Zentrum München, German Research Center for Environmental Health (GmbH), München-Neuherberg
2   Institut für Medizinische Informatik, Statistik und Epidemiologie, Klinikum rechts der Isar, München
› Author Affiliations
› Further Information
Also available at
   Permissions and Reprints

Corrected by:

Erratum: Die frühe Phase der COVID-19-Pandemie in BayernDtsch Med Wochenschr eFirst
DOI: 10.1055/a-1332-4920

Zusammenfassung

Hintergrund Der Effekt von NPIs („nicht pharmakologische Interventionen“) beim Ausbruch von Epidemien ist unbestritten, sowohl bei historischen Ausbrüchen wie auch bei der aktuellen COVID-19-Pandemie. NPIs umfassen Maßnahmen wie Kontaktbeschränkungen oder Hygienevorschriften, die in abgestuften Schritten der aktuellen Lage angepasst werden. Die Auswirkung von NPIs wurde allerdings bisher kaum quantitativ untersucht.

Methoden Aus den offiziellen Fallzahlen des Robert-Koch-Instituts in Berlin sowie Presse- und Twitter-Nachrichten wird eine Rekonstruktion der Frühphase der COVID-19-Pandemie 2020 in Bayern versucht.

Ergebnisse Die ersten COVID-19-Fälle in Deutschland traten bereits Ende Januar in München auf. Während die Primärfälle erfolgreich durch Isolierung und Quarantäne eingegrenzt werden konnten, stellte sich die eigentliche Frühphase der COVID-19-Pandemie ab Ende Februar in 3 Phasen dar, bestehend aus den Winter-/Faschingsferien, den Starkbierfesten in der Folgewoche sowie den Wahlen am 15.03.2020. Der Notstand ab 16.03.2020 markiert das Ende der frühen Ausbreitung. Aus der Analyse der Fallzahlen ergibt sich ein weitgehend zusammenhängendes Bild, auch wenn viele epidemiologische Parameter noch fehlen. Die Ausbreitung begann in den Ferien und ging danach in ein exponentielles Wachstum über. Signifikant mehr Fälle wurden sowohl durch die Starkbierfeste, aber auch durch die bayerische Kommunalwahl registriert, jeweils im Vergleich zu Landkreisen mit der gleichen Prävalenz ohne Exposition. Bayern erreichte damit einen Spitzenplatz der Bundesländer, der sich auch durch restriktive Containment-Maßnahmen in den folgenden Wochen nicht mehr rückgängig machen lässt.

Folgerung Um wirksam zu sein, müssen NPIs frühzeitig, möglichst vor Beginn der exponentiellen Ausbreitung, durchgeführt werden.

Abstract

Introduction The effect of non pharmacological interventions (NPIs) during an epidemic disease outbreak is well accepted dating back to historical events. NPIs involve numerous measurements like hygiene rules or contact restriction that are applied during given situations, while so far only limited quantitative data exist to rate the overall effectiveness.

Methods Using the official counts of Robert Koch Institute in Berlin/Germany, press reports and Twitter messages, the early phase of the current COVID-19/Sars-CoV2 in Bavaria is being reconstructed.

Results The first cases have been observed in Munich by the end of January 2020. While the initial outbreak could be sufficiently covered using isolation and quarantine measurements, the consecutive early spreading falls into three phases, starting with winter school holidays at the end of February, a number of beer festivals in the following week, and general elections on March, 15. The disaster plan on March, 16 indicates the end of the early phase. Using the official case counts, a rather coherent picture evolves although representative epidemiological studies are still missing. The epidemic started with a few cases during the winter holidays, increased exponentially afterwards including significant more cases by beer festivals and another significant excess of cases following the election that occurred in Bavaria only. Compared to other German countries, Bavaria reached the highest prevalence which could not be reversed by even the most restrictive containment measurements.

Conclusion To be effective, NPIs need to applied early, if possible even before the beginning of the exponential phase.

Schlüsselwörter

Epidemie - Pandemie - Virus - COVID-19 - SARS-CoV-2 - Maßnahme - Feste - Kontaktbeschränkung

Key words

epidemic - pandemic - virus - COVID-19 - SARS CoV2 - intervention - festival - contact restriction


Publication History

Article published online:
27 November 2020

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • Literatur

  • 1 Markel H, Lipman HB, Navarro JA. et al. Nonpharmaceutical interventions implemented by US cities during the 1918–1919 influenza pandemic. JAMA 2007; 298: 644-654
    CrossrefPubMedGoogle Scholar
  • 2 Ferguson NM, Nedjati-Gilani G, Imai N. et al Impact of non-pharmaceutical interventions (NPIs) to reduce COVID- 19 mortality and healthcare demand. Report 9, Medical Research Council 2020; DOI: 10.25561/77482.
    CrossrefPubMedGoogle Scholar
  • 3 Smith SM, Sonego S, Wallen GR. et al. Use of non-pharmaceutical interventions to reduce the transmission of influenza in adults: A systematic review. Respirology 2015; 20: 896-903
    CrossrefPubMedGoogle Scholar
  • 4 Sanchez JL, Cooper MJ, Myers CA. et al. Respiratory infections in the US military: Recent experience and control. Clinical Microbiology Reviews 2015; 28: 743-800
    CrossrefPubMedGoogle Scholar
  • 5 Kenney J, Crumly J, Qualls N. Nonpharmaceutical interventions for pandemic influenza: Communication, training, and guidance needs of public health officials. Disaster Medicine and Public Health Preparedness 2019; DOI: 10.1017/dmp.2019.113.
    CrossrefPubMedGoogle Scholar
  • 6 World Health Organisation. Infection prevention and control of epidemic- and pandemic-prone acute respiratory infections in health care(2014). Im Internet (Stand: 29.10.2020: https://apps.who.int/iris/bitstream/handle/10665/112656/9789241507134_eng.pdf;jsessionid=5424576612640085D8AB7DC16E8240AE?sequence=1
    Google Scholar
  • 7 NPGEO Corona. RKI COVID 19. Im Internet: https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0
    Google Scholar
  • 8 Bai J, Perron P. Estimating and testing linear models with multiple structural changes. Econometrica 1998; 47-78
    CrossrefPubMedGoogle Scholar
  • 9 Porta M, Greenland S, Hernán M. et al. A Dictionary of Epidemiology. Oxford University Press; 2014 343.
    Google Scholar
  • 10 Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol 2004; 160: 509-516
    CrossrefPubMedGoogle Scholar
  • 11 Tufte ER. The visual display of quantitative information. Graphics Press; 1983
    Google Scholar
  • 12 Böhmer MM, Buchholz U, Corman VM. et al. Investigation of a COVID-19 outbreak in Germany resulting from a single travel-associated primary case: a case series. Lancet Infect Dis 2020; 20: 920-928
    CrossrefPubMedGoogle Scholar
  • 13 Gudbjartsson DF, Helgason A, Jonsson H. et al. Spread of SARS-Cov-2 in the Icelandic Population. N Engl J Med 2020; 382: 2302-2315
    Google Scholar
  • 14 Burkert M. Neue Erkenntnisse aus der Bier-Geografie. Regionalökonomische Bedeutung der Herstellung von Bier in Ober-und Mittelfranken. 31. Heiligenstädter Gespräche. 2018: 35-42
    Google Scholar
  • 15 Day M. COVID-19: four fifths of cases are asymptomatic, China figures indicate. BMJ 2020; 369: 1375
    CrossrefPubMedGoogle Scholar
  • 16 Robert-Koch-Institut. Ergebnisse der Untersuchung der COVID-19-Epidemie im Landkreis Tischenreuth (April-Juni 2020). Im Internet (Stand: 29.10.2020): www.kreis-tir.de/fileadmin/user_upload/rki_ergebnis.pdf
    Google Scholar
  • 17 Lai S, Ruktanonchai NW, Zhou L. et al. Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China. Nature 2020; 585: 410-413
    Google Scholar
  • 18 Nussbaumer-Streit B, Mayr V, Dobrescu AI. et al. Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review. Cochrane Database Syst Rev 2020; 4: CD013574
    CrossrefPubMedGoogle Scholar
  • 19 Banholzer N, van Weenen E, Kratzwald B. et al. Estimating the impact of non-pharmaceutical interventions on documented infections with COVID-19: A cross-country analysis. Nature 2020; 584: 257-261
    CrossrefPubMedGoogle Scholar
  • 20 Pei S, Kandula S, Shaman J. Differential Effects of Intervention Timing on COVID-19 Spread in the United States (preprint 2020). Im Internet (Stand 29.10.2020: https://www.medrxiv.org/content/10.1101/2020.05.15.20103655v1.full.pdf
    Google Scholar
  • 21 Woelfel R, Corman VM, Guggemos W. et al Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster (preprint 2020). Im Internet (Stand: 29.10.2020: https://www.medrxiv.org/content/10.1101/2020.03.05.20030502v1
    Google Scholar
  • 22 Wang Q, Zhang Y, Wu L. et al. Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2. Cell 2020; 181: 894-904
    CrossrefPubMedGoogle Scholar
  • 23 Barton CM, Alberti M, Ames D. et al. Call for transparency of COVID-19 models. Science 2020; 368: 482-483
    CrossrefPubMedGoogle Scholar
  • 24 Odone ADD, Scognamiglio T, Signorelli C. COVID-19 deaths in Lombardy, Italy: data in context. Lancet 2020; 5: E310
    PubMedGoogle Scholar
  • 25 The PLOS Medicine Editors. Pandemic responses: Planning to neutralize SARS-CoV-2 and prepare for future outbreaks. PLoS Med 2020; 17: e1003123
    CrossrefPubMedGoogle Scholar
  • 26 Aledort JE, Lurie N, Wasserman J. et al. Non-pharmaceutical public health interventions for pandemic influenza: an evaluation of the evidence base. BMC Public Health 2007; 7: 208
    CrossrefPubMedGoogle Scholar
  • 27 Davidson AD, Williamson MK, Lewis S. et al Characterisation of the transcriptome and proteome of SARS-CoV-2 using direct RNA sequencing and tandem mass spectrometry reveals evidence for a cell passage induced in-frame deletion in the spike glycoprotein that removes the furin-like cleavage site (preprint 2020). Im Internet (Stand: 29.10.2020: https://www.biorxiv.org/content/10.1101/2020.03.22.002204v1
    Google Scholar
  • 28 Yan T, Xiao R, Lin G. Angiotensin‐converting enzyme 2 in severe acute respiratory syndrome coronavirus and SARS‐CoV‐2: A double‐edged sword. FASEB J 2020; 34: 6017-6026
    CrossrefPubMedGoogle Scholar