  Cross-sectional HIV incidence estimation leverages recency test results to
determine the HIV incidence of a population of interest, where recency test
uses biomarker profiles to infer whether an HIV-positive individual was
"recently" infected. This approach possesses an obvious advantage over the
conventional cohort follow-up method since it avoids longitudinal follow-up and
repeated HIV testing. In this manuscript, we consider the extension of
cross-sectional incidence estimation to estimate the incidence of a different
target population addressing potential population heterogeneity. We propose a
general framework that incorporates two settings: one with the target
population that is a subset of the population with cross-sectional recency
testing data, e.g., leveraging recency testing data from screening in
active-arm trial design, and the other with an external target population. We
also propose a method to incorporate HIV subtype, a special covariate that
modifies the properties of recency test, into our framework. Through extensive
simulation studies and a data application, we demonstrate the excellent
performance of the proposed methods. We conclude with a discussion of
sensitivity analysis and future work to improve our framework.
