A within-host-informed event-time framework linking epidemiological delay distributions

Understanding when and how pathogens spread from person to person is fundamental to characterizing transmission, interpreting surveillance data, and designing effective control measures. Yet key epidemiological delays -- including the latent period, incubation period, generation interval, and serial interval -- are difficult to estimate because key events such as infection, infectiousness, symptom onset, and transmission are rarely observed directly. Current approaches typically estimate these delays using flexible statistical distributions rather than explicitly linking them through the underlying processes that generate them, making it difficult to interpret the underlying biology and to ensure consistency across related delay estimates. Here, we develop a stochastic event-time framework built around a dynamic within-host pathogen-growth model that treats epidemiological delays as coupled outcomes of a shared biological process. By representing these key events as linked stochastic events, the framework produces internally consistent delay distributions while inferring parameters with mechanistic interpretations conditional on the assumed within-host model that govern pathogen growth, clearance, infectiousness, and symptom onset. The framework also accommodates censored and partially observed infection and symptom-onset times through a unified observation model. Across simulation studies, the framework accurately recovered generation-interval characteristics across sample sizes and disease time scales. In applications to SARS-CoV-2 and mpox transmission-pair data, the framework showed improved predictive fit relative to conventional lognormal delay models in several analyses, although the magnitude of improvement varied across datasets and observation models; it estimated shorter generation intervals for Omicron than Delta and revealed alternative within-host parameters compatible with the mpox transmission data. This mechanistic framework provides a biologically interpretable alternative to independently fitted delay distributions, yields internally consistent estimates of epidemiological delays, and establishes a foundation for integrating biological and epidemiological data to better understand, predict, and control pathogen transmission.