Identification and validation of a multi-assay algorithm for cross-sectional HIV incidence estimation in populations with subtype C infection.

Oliver LaeyendeckerJacob KonikoffDouglas E MorrisonRonald BrookmeyerJing WangConnie CelumCharles S MorrisonQuarraisha Abdool KarimAudrey E Pettifor Susan H Eshleman

Published in: Journal of the International AIDS Society (2019)

An optimal MAA was identified for cross-sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance.

Keyphrases

antiretroviral therapy
hiv positive
cross sectional
hiv infected
hiv testing
human immunodeficiency virus
hepatitis c virus
hiv aids
men who have sex with men
machine learning
risk factors
deep learning
south africa
public health
high throughput