title: Reassessment of HIV-1 acute phase infectivity: accounting for heterogeneity and study design with simulated cohorts.
abstract: BACKGROUND
  The infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk.
  
  
  RESULTS
  We estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (dacute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own. From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79-57) and dacute = 1.7 mo (95% CrI: 0.55-6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) × dacute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3-9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design. Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure.
  
  
  CONCLUSIONS
  Previous EHMacute estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses.
authors.affiliation: Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Steve E
authors.initials: SE
authors.lastname: Bellan
authors.affiliation: Department of Biology, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Dushoff
authors.affiliation: Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America; Department of Ecology and Evolution, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Alison P
authors.initials: AP
authors.lastname: Galvani
authors.affiliation: Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America; The Santa Fe Institute, Santa Fe, New Mexico, United States of America.
authors.forename: Lauren Ancel
authors.initials: LA
authors.lastname: Meyers
mesh_terms: D000208:Acute Disease; D000210:Acute-Phase Reaction; D001499:Bayes Theorem; D015982:Bias; D015331:Cohort Studies; D003198:Computer Simulation; D005191:Family Characteristics; D015658:HIV Infections / Q000473:pathology / Q000635:transmission* / Q000821:virology; D006679:HIV Seropositivity / Q000821:virology; D015497:HIV-1; D020010:Heterosexuality; D006801:Humans; D018579:Patient Selection; D012107:Research Design / Q000706:statistics & numerical data; D012189:Retrospective Studies; D012306:Risk; D012725:Sexual Behavior; D012747:Sexual Partners; D014454:Uganda; D019562:Viral Load