Predicting the Risk of Human Immunodeficiency Virus Type 1 (HIV-1) Acquisition in Rural South Africa Using Geospatial Data.

D Allen RobertsDiego CuadrosAlain VandormaelDickman GaretaRuanne V BarnabasKobus HerbstFrank TanserAdam Akullian

Published in: Clinical infectious diseases : an official publication of the Infectious Diseases Society of America (2022)

Geospatial models with no individual measures other than age group predicted HIV risk nearly as well as models that included detailed behavioral data. Geospatial models may help guide HIV prevention efforts to individuals and geographic areas at highest risk.

Keyphrases

human immunodeficiency virus
antiretroviral therapy
hiv positive
south africa
hepatitis c virus
hiv infected
hiv aids
electronic health record
hiv testing
big data
men who have sex with men
machine learning
data analysis