Between 2013-2017, 75% of residents in 16 communities in the SEARCH Study tested annually for HIV. In this population, we evaluated three strategies for using demographic factors to predict the one-year risk of HIV seroconversion: (1) membership in 1 known "Risk Group" (e.g., young woman or HIV-infected spouse); (2) a "Model-based" risk score constructed with logistic regression; (3) a "Machine Learning" risk score constructed with the Super Learner algorithm. We hypothesized Machine Learning would identify high-risk individuals more efficiently (fewer persons targeted for a fixed sensitivity) and with higher sensitivity (for a fixed number of persons targeted) than either other approach.

This is a guest post by Chris Adzima, a Senior Information Systems Analyst for the Washington County Sheriff's Office. In law enforcement, it is extremely important to identify persons of interest quickly. In most cases, this is accomplished by showing a picture of the person to multiple law enforcement officers in hopes that someone knows the person. In Washington County, Oregon, there are nearly 20,000 different bookings (when a person is processed into the jail) every year. As time passes, officers' memories of individual bookings fade.