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

Autor: Brockhaus EK; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany., Wolffram D; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.; Computational Statistics Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany., Stadler T; Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.; Swiss Institute of Bioinformatics, Lausanne, Switzerland., Osthege M; Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.; Institute of Biotechnology, RWTH Aachen University, Aachen, Germany., Mitra T; Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Braunschweig, Germany.; Current address: Kennedy Institute of Rheumatology, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom., Littek JM; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany., Krymova E; Swiss Data Science Center, EPF Lausanne and ETH Zurich, Zurich, Switzerland., Klesen AJ; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany., Huisman JS; Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.; Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America., Heyder S; Institute of Mathematics, Technische Universität Ilmenau, Ilmenau, Germany., Helleckes LM; Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.; Institute of Biotechnology, RWTH Aachen University, Aachen, Germany., An der Heiden M; Robert Koch Institute, Berlin, Germany., Funk S; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom., Abbott S; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom., Bracher J; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.; Computational Statistics Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Nov 27; Vol. 19 (11), pp. e1011653. Date of Electronic Publication: 2023 Nov 27 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1011653
Abstrakt: The effective reproductive number Rt 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. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates.
Competing Interests: The authors declare that there are no competing interests.
(Copyright: © 2023 Brockhaus et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje