ABSTRACT
Introduction: Tracing transmission paths and identifying infection sources have been effective in curbing the spread of coronavirus disease 2019 (COVID-19). However, when facing a large-scale outbreak, this is extremely time-consuming and labor-intensive, and resources for infection source tracing become limited. In this study, we aimed to use knowledge graph (KG) technology to automatically infer transmission paths and infection sources. Methods: We constructed a KG model to automatically extract epidemiological information and contact relationships from case reports. We then used an inference engine to identify transmission paths and infection sources. To test the model's performance, we used data from two COVID-19 outbreaks in Beijing. Results: The KG model performed well for both outbreaks. In the first outbreak, 20 infection relationships were identified manually, while 42 relationships were determined using the KG model. In the second outbreak, 32 relationships were identified manually and 31 relationships were determined using the KG model. All discrepancies and omissions were reasonable. Discussion: The KG model is a promising tool for predicting and controlling future COVID-19 epidemic waves and other infectious disease pandemics. By automatically inferring the source of infection, limited resources can be used efficiently to detect potential risks, allowing for rapid outbreak control.
ABSTRACT
Although the concept of "myeloid neoplasm continuum" has long been proposed, few comparative genomics studies directly tested this hypothesis. Here we report a multi-modal data analysis of 730 consecutive newly diagnosed patients with primary myeloid neoplasm, along with 462 lymphoid neoplasm cases serving as the outgroup. Our study identified a "Pan-Myeloid Axis" along which patients, genes, and phenotypic features were all aligned in sequential order. Utilizing relational information of gene mutations along the Pan-Myeloid Axis improved prognostic accuracy for complete remission and overall survival in adult patients of de novo acute myeloid leukemia and for complete remission in adult patients of myelodysplastic syndromes with excess blasts. We submit that better understanding of the myeloid neoplasm continuum might shed light on how treatment should be tailored to individual diseases. Significance: The current criteria for disease diagnosis treat myeloid neoplasms as a group of distinct, separate diseases. This work provides genomics evidence for a "myeloid neoplasm continuum" and suggests that boundaries between myeloid neoplastic diseases are much more blurred than previously thought.
