Is a More Data-driven Approach the Future of Tuberculosis Transmission Modeling?

Abstract

In their paper in this issue of Clinical Infectious Diseases, Nelson et al [1] bring the powerful toolkit of exponential random graph models (ERGMs) to the analysis of transmission networks derived from whole-genome sequence (WGS) data collected during an outbreak of extensively drug-resistant tuberculosis (XDR-TB) in KwaZulu-Natal, South Africa. ERGMs allow for the analysis of connections in network data using a regression-like framework, while adjusting for the inherent nonindependence of network linkages [2].

Publication
Clinical Infectious Diseases
Jon Zelner
Jon Zelner
Assistant Professor

My research interests include distributed robotics, mobile computing and programmable matter.