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. 2022 Nov 11;22:839. doi: 10.1186/s12879-022-07777-6

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Illustration of different approaches for SARS-CoV-2 variant exposure status ascertainment: (1) exposure defined based on WGS, (2) exposure defined based on WGS or presumptive genotyping and (3) exposure defined based on time periods with known variant circulation. The different approaches are compared in terms of the obtained sample sizes, the inference estimates and their 95% confidence intervals (expressed as standardized risk and risk ratio) for severe COVID-19 (experiencing an acute respiratory distress syndrome (ARDS) event, being admitted to the intensive care unit (ICU), or in-hospital mortality), and the distribution over time of the number of hospitalized COVID-19 patients included in the analysis by week of diagnosis (7-day rolling average). Please note the scale differences in the y-axis in the distribution plots