  Accurate HIV incidence estimation based on individual recent infection status
(recent vs long-term infection) is important for monitoring the epidemic,
targeting interventions to those at greatest risk of new infection, and
evaluating existing programs of prevention and treatment. Starting from 2015,
the Population-based HIV Impact Assessment (PHIA) individual-level surveys are
implemented in the most-affected countries in sub-Saharan Africa. PHIA is a
nationally-representative HIV-focused survey that combines household visits
with key questions and cutting-edge technologies such as biomarker tests for
HIV antibody and HIV viral load which offer the unique opportunity of
distinguishing between recent infection and long-term infection, and providing
relevant HIV information by age, gender, and location. In this article, we
propose a semi-supervised logistic regression model for estimating individual
level HIV recency status. It incorporates information from multiple data
sources -- the PHIA survey where the true HIV recency status is unknown, and
the cohort studies provided in the literature where the relationship between
HIV recency status and the covariates are presented in the form of a
contingency table. It also utilizes the national level HIV incidence estimates
from the epidemiology model. Applying the proposed model to Malawi PHIA data,
we demonstrate that our approach is more accurate for the individual level
estimation and more appropriate for estimating HIV recency rates at aggregated
levels than the current practice -- the binary classification tree (BCT).
