By developing blood biomarkers and «immunologic signatures» related to antigen - specific T - cell responses, the researchers hope to
identify individuals with
latent TB infection who are at greatest risk for progression to active disease, allowing development of prevention strategies to target those at highest risk in areas with high rates of infection (usually low - and middle - income countries), as well as high income countries such as the U.S., where
factors such as recent infection and HIV co-infection are associated with an increased risk of progression to active TB.
Algorithms function by prioritizing certain
factors —
identifying statistical patterns from observed and
latent variables and subsequently offering «if this, then that» conclusions.