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Abstract Background HIV transmission is characterised by a low per-act probability, a relatively high proportion of multiple variant transmission events, and a plateauing of transmission risk at high viral loads. No existing mechanistic model can simultaneously recapitulate all of these observations, thereby limiting our ability to predict unobserved transmission phenomena and evaluate prevention strategies. Methods We developed a suite of mathematical models that encode an empirically plausible set of transmission mechanisms and then fit these models within a Bayesian framework to available epidemiological data to identify which set of mechanisms are sufficient to recapitulate the data. Following formal model comparison, we embedded the best-fit model into a phylodynamic framework and calibrated it using Approximate Bayesian Computation, to assess whether phylogenetic trees from individual transmission pairs were both consistent with the model and informative. Finally, we further validated our most likely model against two large prospective studies (PARTNER1 and STEP). Results Our calibrated model predicts that for each systemic infection, approximately four to five transient infections occur—exposure events in which viral replication occurs but is stochastically extinguished—consistent with indirect empirical evidence from the STEP vaccine trial. The model predicts a transmission rate of fewer than 0.05 systemic infections per 100 couple-years follow up from individuals with undetectable viral load, providing a mechanistic basis for the negligible risk observed in the PART-NER1 study. The model also predicts a strong link between the number of viral particles transmitted and the number of variants establishing infection, modulated by the transmitter’s infection stage. Recalibrating for men who have sex with men indicated that higher transmission rates in this population are explained by a single parameter: a greater probability of permissive conditions for infection. These predictions emerge from a model in which three mechanisms were needed to explain the epidemiological data: highly infrequent permissive conditions within the exposed partner, stage-dependent differences in the probability that infected cells establish systemic infection, and target cell limitation at the site of infection. The model was further validated against phylogenetic data from 48 transmission pairs, where combining mechanistic and phylogenetic information sharpened posterior estimates of time since infection in the majority of cases. Conclusion Three biologically grounded mechanisms are sufficient to explain the key features of HIV transmission. The resulting model provides a principled and mechanistic basis for estimating transmission risk and for designing interventions to reduce it.