Data_Sheet_1_Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases.pdf

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.

解析结核病（tuberculosis, TB）传播链，可帮助公共卫生人员精准配置资源以阻断进一步传播，但目前尚缺乏可自动化完成该流程的工具。我们研发了逻辑推断结核病传播（Logically Inferred Tuberculosis Transmission, LITT）算法，以系统化整合并分析全基因组测序、临床及流行病学数据。该算法基于结核聚集性疫情调查中通常需人工完成的工作流程，可识别聚集性疫情中每一例病例的潜在传染源，并对其进行排序分级。我们采用由美国三个不同管辖区域内开展本地调查的56个聚集性疫情（病例规模范围2~69例）中共534例病例构成的多样化数据集，对LITT算法进行了性能评估。在181例病例中，145例（占比80%）的最可能传染源判定结果与人工调查人员的结论一致。通过对二者结论不一致的案例进行复盘分析，我们发现剩余分歧多源于LITT算法所用数据集存在错误。此外，我们还开发了图形用户界面、用户手册及培训资源，以提升一线工作人员对LITT算法的可及性。尽管LITT算法无法替代全面的现场调查，但在结核聚集性疫情调查过程中，该算法可帮助调查人员系统化地分析并解读复杂数据。代码开源地址：https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0；https://zenodo.org/badge/latestdoi/166261171.