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

Frontiers in Public Health

Lauren Cowan¹  James Posey¹  Sue Reynolds¹  Kala M. Raz¹  Benjamin J. Silk¹  Steve Kammerer¹  Clinton J. McDaniel¹  Sarah Talarico¹  Kathryn Winglee¹  Shameer Poonja²  Wendy Noboa²  Jillian Knorr³  Jeanne Sullivan Meissner³  Lauren Linde⁴  Tambi Shaw⁴  Martin Cilnis⁴ 

[1] Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA, United States;Los Angeles County Department of Public Health, Los Angeles, CA, United States;New York City Department of Health and Mental Hygiene, Queens, NY, United States;TB Control Branch, California Department of Public Health, Richmond, CA, United States;

关键词: tuberculosis;    whole-genome sequencing;    transmission;    genomic epidemiology;    cluster investigation;   

DOI  :  10.3389/fpubh.2021.667337

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown