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. 2022 Jun 29;2:77. doi: 10.1038/s43856-022-00145-0

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© The Author(s) 2022

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — We present both the original data and the fitted bumps we use for modeling the disruption, along with the diagnosis rates in two scenarios: the baseline, with no disruption (dotted lines), and the pandemic scenario, where the drop in diagnosis happens and is followed by a return of the diagnosis rates to the baseline scenario. Diagnosis multipliers are obtained directly using the WHO data as the TB notifications in that period divided by the mean of TB notifications in the year 2019. The four countries considered are (a) India, (b) Indonesia, (c) Kenya, (d) Pakistan.