Frontiers in Public Health (Jun 2021)

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases

  • Kathryn Winglee,
  • Clinton J. McDaniel,
  • Lauren Linde,
  • Steve Kammerer,
  • Martin Cilnis,
  • Kala M. Raz,
  • Wendy Noboa,
  • Wendy Noboa,
  • Jillian Knorr,
  • Lauren Cowan,
  • Sue Reynolds,
  • James Posey,
  • Jeanne Sullivan Meissner,
  • Shameer Poonja,
  • Shameer Poonja,
  • Tambi Shaw,
  • Sarah Talarico,
  • Benjamin J. Silk

DOI
https://doi.org/10.3389/fpubh.2021.667337
Journal volume & issue
Vol. 9

Abstract

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Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2–69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation.Code available at:https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171.

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