Supporting Information files S1 Table, S1 Example, and S1 Details are incorrectly published in raw TeX format rather than PDF format. Please see the formatted PDF files here.
Supporting Information
S1 Table. HIV Incidence and undiagnosed fraction estimates broken down by race/ethnicity.
Estimates of the number of undiagnosed HIV cases among MSM in King County stratified by ethnicity. * Sum of cases thought to reside in King County based on HIV surveillance data (N = 4188, 458, and 572 respectively) and the estimated number of undiagnosed cases.
https://doi.org/10.1371/journal.pone.0135878.s001
(PDF)
S1 Example. Constant Incidence Calculation.
This example uses a very simple case to show the logic behind the constant incidence calculation.
https://doi.org/10.1371/journal.pone.0135878.s002
(PDF)
S1 Details. Additional details on the backcalculation algorithm.
This provides more detail on the basic backcalculation algorithm, accounting for limited surveillance windows, and the quadratic smoothing penalty.
https://doi.org/10.1371/journal.pone.0135878.s003
(PDF)
Reference
- 1. Fellows IE, Morris M, Birnbaum JK, Dombrowski JC, Buskin S, Bennett A, et al. (2015) A New Method for Estimating the Number of Undiagnosed HIV Infected Based on HIV Testing History, with an Application to Men Who Have Sex with Men in Seattle/King County, WA. PLoS ONE 10(7): e0129551. pmid:26196132
Citation: Fellows IE, Morris M, Birnbaum JK, Dombrowski JC, Buskin S, Bennett A, et al. (2015) Correction: A New Method for Estimating the Number of Undiagnosed HIV Infected Based on HIV Testing History, with an Application to Men Who Have Sex with Men in Seattle/King County, WA. PLoS ONE 10(8): e0135878. https://doi.org/10.1371/journal.pone.0135878
Published: August 12, 2015
Copyright: © 2015 Fellows et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited