Description: Germany’s next shutdown—Possible scenarios and outcomes

This title appears in the Scientific Report : 2021

Germany’s next shutdown—Possible scenarios and outcomes

In attempting to predict the further course of the novel coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2, mathematical models of different types are frequently employed and calibrated to reported case numbers. Among the major challenges in interpreting these data is the uncertainty abou...

Personal Name(s):	Barbarossa, Maria Vittoria (Corresponding author)
	Fuhrmann, Jan
Contributing Institute:	Jülich Supercomputing Center; JSC
Published in:	Influenza and other respiratory viruses, 15 (2021) 3, S. 326-330
Imprint:	Oxford [u.a.] Wiley-Blackwell 2021
DOI:	10.1111/irv.12827
Document Type:	Journal Article
Research Program:	Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups Enabling Computational- & Data-Intensive Science and Engineering
Link:	OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.1111/irv.12827 in citations.
Please use the identifier: http://hdl.handle.net/2128/27656 in citations.

In attempting to predict the further course of the novel coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2, mathematical models of different types are frequently employed and calibrated to reported case numbers. Among the major challenges in interpreting these data is the uncertainty about the amount of undetected infections, or conversely: the detection ratio. As a result, some models make assumptions about the percentage of detected cases among total infections while others completely neglect undetected cases. Here, we illustrate how model projections about case and fatality numbers vary significantly under varying assumptions on the detection ratio. Uncertainties in model predictions can be significantly reduced by representative testing, both for antibodies and active virus RNA, to uncover past and current infections that have gone undetected thus far.