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. 2019 Mar 7;9:3810. doi: 10.1038/s41598-019-39799-7

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© The Author(s) 2019

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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Sensitivity analysis for key assumptions in the model. (A) Effect of assumptions for how TB infectivity varies during the clinical course. Shown is the impact of a PPSA at 75% coverage in Mumbai (percent cases averted over ten years). The x-axis shows a range of scenarios for the infectivity during the patient delay, relative to that during the diagnostic delay. (B,C) Identifying key elements of private provider behaviour. The figures show the drop in overall impact that results, when a PPSA that fails to improve the provider behaviour shown (while addressing all remaining provider behaviours). For clarity, plots show only the four most important factors in each setting. Bar colours denote different provider types, as shown in the right-hand legend. Panel (B) shows results for Mumbai, while panel (C) shows results for Patna.