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Estimation of R0 for the spread of SARS-CoV-2 in Germany from excess mortality.
Prada, Juan Pablo; Maag, Luca Estelle; Siegmund, Laura; Bencurova, Elena; Chunguang, Liang; Koutsilieri, Eleni; Dandekar, Thomas; Scheller, Carsten.
  • Prada JP; Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074, Würzburg, Germany.
  • Maag LE; Institute of Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany.
  • Siegmund L; Institute of Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany.
  • Bencurova E; Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074, Würzburg, Germany.
  • Chunguang L; Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074, Würzburg, Germany.
  • Koutsilieri E; Institute of Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany.
  • Dandekar T; Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074, Würzburg, Germany. dandekar@biozentrum.uni-wuerzburg.de.
  • Scheller C; Institute of Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany. scheller@vim.uni-wuerzburg.de.
Sci Rep ; 12(1): 17221, 2022 Oct 14.
Article in English | MEDLINE | ID: covidwho-2077104
ABSTRACT
For SARS-CoV-2, R0 calculations in the range of 2-3 dominate the literature, but much higher estimates have also been published. Because capacity for RT-PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of RT-PCR testing capacity. We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of RT-PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). The uncorrected R0 value for the spread of SARS-CoV-2 based on RT-PCR incidence data was 2.56 (95% CI 2.52-2.60) for Covid-19 cases and 2.03 (95% CI 1.96-2.10) for Covid-19-related deaths. However, because the number of RT-PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32-1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0January = 1.68 and a minimum value of R0July = 1.01. Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Basic Reproduction Number / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-22101-7

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Basic Reproduction Number / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-22101-7