title: Validation of the modes of transmission model as a tool to prioritize HIV prevention targets: a comparative modelling analysis.
abstract: BACKGROUND
  The static Modes of Transmission (MOT) model predicts the annual fraction of new HIV infections acquired across subgroups (MOT metric), and is used to focus HIV prevention. Using synthetic epidemics via a dynamical model, we assessed the validity of the MOT metric for identifying epidemic drivers (behaviours or subgroups that are sufficient and necessary for HIV to establish and persist), and the potential consequence of MOT-guided policies.
  
  
  RESULTS
  To generate benchmark MOT metrics for comparison, we simulated three synthetic epidemics (concentrated, mixed, and generalized) with different epidemic drivers using a dynamical model of heterosexual HIV transmission. MOT metrics from generic and complex MOT models were compared against the benchmark, and to the contribution of epidemic drivers to overall HIV transmission (cumulative population attributable fraction over t years, PAFt). The complex MOT metric was similar to the benchmark, but the generic MOT underestimated the fraction of infections in epidemic drivers. The benchmark MOT metric identified epidemic drivers early in the epidemics. Over time, the MOT metric did not identify epidemic drivers. This was not due to simplified MOT models or biased parameters but occurred because the MOT metric (irrespective of the model used to generate it) underestimates the contribution of epidemic drivers to HIV transmission over time (PAF5-30). MOT-directed policies that fail to reach epidemic drivers could undermine long-term impact on HIV incidence, and achieve a similar impact as random allocation of additional resources.
  
  
  CONCLUSIONS
  Irrespective of how it is obtained, the MOT metric is not a valid stand-alone tool to identify epidemic drivers, and has limited additional value in guiding the prioritization of HIV prevention targets. Policy-makers should use the MOT model judiciously, in combination with other approaches, to identify epidemic drivers.
authors.affiliation: Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom; St. Michael's Hospital, Department of Medicine, University of Toronto, Toronto, Canada.
authors.forename: Sharmistha
authors.initials: S
authors.lastname: Mishra
authors.affiliation: Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom.
authors.forename: Michael
authors.initials: M
authors.lastname: Pickles
authors.affiliation: Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.
authors.forename: James F
authors.initials: JF
authors.lastname: Blanchard
authors.affiliation: Centre for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada.
authors.forename: Stephen
authors.initials: S
authors.lastname: Moses
authors.affiliation: Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom.
authors.forename: Zara
authors.initials: Z
authors.lastname: Shubber
authors.affiliation: Department of Infectious Disease Epidemiology, Imperial College, London, United Kingdom.
authors.forename: Marie-Claude
authors.initials: MC
authors.lastname: Boily
mesh_terms: D003140:Communicable Disease Control / Q000379:methods*; D062665:Epidemiological Monitoring; D006678:HIV / Q000472:pathogenicity*; D015658:HIV Infections / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission*; D006801:Humans; D015994:Incidence; D008962:Models, Theoretical