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Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

Brockhaus, Elisabeth K. 1; Wolffram, Daniel 1; Stadler, Tanja; Osthege, Michael; Mitra, Tanmay; Littek, Jonas M. 1; Krymova, Ekaterina; Klesen, Anna J. 1; Huisman, Jana S.; Heyder, Stefan; Helleckes, Laura M.; an der Heiden, Matthias; Funk, Sebastian; Abbott, Sam; Bracher, Johannes 1
1 Institut für Volkswirtschaftslehre (ECON), Karlsruher Institut für Technologie (KIT)

Abstract:

The effective reproductive number R$_t$ has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000166984
Veröffentlicht am 08.01.2024
Originalveröffentlichung
DOI: 10.1371/journal.pcbi.1011653
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 1553-734X, 1553-7358
KITopen-ID: 1000166984
Erschienen in PLOS Computational Biology
Verlag Public Library of Science (PLoS)
Band 19
Heft 11
Seiten Art.-Nr.: e1011653
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 27.11.2023
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Scopus
Web of Science
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