title: Viral load monitoring of antiretroviral therapy, cohort viral load and HIV transmission in Southern Africa:  a mathematical modelling analysis.
abstract: OBJECTIVE
  In low-income settings, treatment failure is often identified using CD4 cell count monitoring. Consequently, patients remain on a failing regimen, resulting in a higher risk of transmission. We investigated the benefit of routine viral load monitoring for reducing HIV transmission.
  
  
  METHODS
  Mathematical model.
  
  
  METHODS
  We developed a stochastic mathematical model representing the course of individual viral load, immunological response and survival in a cohort of 1000 HIV-infected patients receiving antiretroviral therapy (ART) in southern Africa. We calculated cohort viral load (CVL; sum of individual viral loads) and used a mathematical relationship between individual viral load values and transmission probability to estimate the number of new HIV infections. Our model was parameterized with data from the International epidemiologic Databases to Evaluate AIDS Southern African collaboration. Sensitivity analyses were performed to assess the validity of the results in a universal 'test and treat' scenario, wherein patients start ART earlier after HIV infection.
  
  
  RESULTS
  If CD4 cell count alone was regularly monitored, the CVL was 2.6 × 10 copies/ml and the treated patients transmitted on average 6.3 infections each year. With routine viral load monitoring, both CVL and transmissions were reduced by 31% to 1.7 × 10 copies/ml and 4.3 transmissions, respectively. The relative reduction of 31% between monitoring strategies remained similar for different scenarios.
  
  
  CONCLUSIONS
  Although routine viral load monitoring enhances the preventive effect of ART, the provision of ART to everyone in need should remain the highest priority.
authors.affiliation: Institute of Social and Preventive Medicine, University of Bern, Switzerland.
authors.forename: Janne
authors.initials: J
authors.lastname: Estill
authors.forename: Cindy
authors.initials: C
authors.lastname: Aubrière
authors.forename: Matthias
authors.initials: M
authors.lastname: Egger
authors.forename: Leigh
authors.initials: L
authors.lastname: Johnson
authors.forename: Robin
authors.initials: R
authors.lastname: Wood
authors.forename: Daniela
authors.initials: D
authors.lastname: Garone
authors.forename: Thomas
authors.initials: T
authors.lastname: Gsponer
authors.forename: Gilles
authors.initials: G
authors.lastname: Wandeler
authors.forename: Andrew
authors.initials: A
authors.lastname: Boulle
authors.forename: Mary-Ann
authors.initials: MA
authors.lastname: Davies
authors.forename: Timothy B
authors.initials: TB
authors.lastname: Hallett
authors.forename: Olivia
authors.initials: O
authors.lastname: Keiser
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy* / Q000635:transmission*; D000353:Africa, Southern / Q000453:epidemiology; D000465:Algorithms; D019380:Anti-HIV Agents / Q000008:administration & dosage* / Q000009:adverse effects; D018791:CD4 Lymphocyte Count; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D019562:Viral Load / Q000187:drug effects
chemical_list: D019380:Anti-HIV Agents