Nelson, Kristin N.Gandhi, Neel R.Mathema, BarunLopman, Benjamin A.Brust, James C.M.Auld, Sara C.Ismail, Nazir AhmedOmar, Shaheed VallyBrown, Tyler S.Allana, SalimCampbell, AngieMoodley, PraviMlisana, KolekaShah, N. SaritaJenness, Samuel M.2021-08-032021-08-032020-07Nelson, K.N., Gandhi, N.R., Mathema, B. et al. 2020, 'Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa', American Journal of Epidemiology, vol. 189, no. 7, pp. 735-745.0002-9262 (print)1476-6256 (online)10.1093/aje/kwaa028http://hdl.handle.net/2263/81099This work was presented at the Seventh International Conference on Infectious Disease Dynamics (Epidemics7), Charleston, South Carolina, December 3–6, 2019.Patterns of transmission of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases worldwide in 2017. Modeling TB transmission networks can provide insight into drivers of transmission, but incomplete sampling of TB cases can pose challenges for inference from individual epidemiologic and molecular data. We assessed the effect of missing cases on a transmission network inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases in KwaZulu-Natal, South Africa, diagnosed in 2011–2014. We tested scenarios in which cases were missing at random, missing differentially by clinical characteristics, or missing differentially by transmission (i.e., cases with many links were under- or oversampled). Under the assumption that cases were missing randomly, the mean number of transmissions per case in the complete network needed to be larger than 20, far higher than expected, to reproduce the observed network. Instead, the most likely scenario involved undersampling of high-transmitting cases, and models provided evidence for super-spreading. To our knowledge, this is the first analysis to have assessed support for different mechanisms of missingness in a TB transmission study, but our results are subject to the distributional assumptions of the network models we used. Transmission studies should consider the potential biases introduced by incomplete sampling and identify host, pathogen, or environmental factors driving super-spreading.en© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in American Journal of Epidemiology following peer review. The definitive publisher-authenticated version is : 'Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa', American Journal of Epidemiology, vol. 189, no. 7, pp. 735-745, 2020. doi : 10.1093/aje/kwaa028, is available online at : https://academic.oup.com/aje.Tuberculosis (TB)Bias analysisDrug-resistant tuberculosis (DR-TB)Missing dataNetwork modelingTuberculosis transmissionWhole genome sequencing (WGS)Mycobacterium tuberculosis (MTB)South Africa (SA)Extensively drug-resistant (XDR)Postprint Article