A within-host-informed event-time framework linking epidemiological delay distributions
- 1The University of Texas at Austin
Abstract
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.
Author summaryFor many infections that spread directly from person to person, the timing of infection, infectiousness, symptom onset, and passing the infection to someone else is central to understanding how disease spreads. These delays are often estimated separately using statistical distributions, even though they are biologically connected. For example, the amount of pathogen in the body can influence when someone becomes infectious, when symptoms appear, and when transmission is most likely. We developed a modeling framework that links these timings through a shared pathogen growth process. In the model, becoming infectious, developing symptoms, and transmitting infection occur as chance events whose rates change with pathogen load. We tested the framework using simulations and data from recent SARS-CoV-2 and mpox epidemics, showing that it outperformed conventional statistical approaches and can incorporate pathogenload data into delay estimation, while providing new estimates of the generation intervals of recent COVID-19 variants and mpox. More broadly, the framework provides a way to combine epidemiological data with biological information, helping researchers understand how changes in within-host (WH) pathogen dynamics influence transmission and improving our ability to predict and control infectious disease outbreaks.
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BibTeX
@misc{jamieson2026withinhostinformed,
title = {{A within-host-informed event-time framework linking epidemiological delay distributions}},
author = {N. Jamieson and S. W. Park and K. Ainslie and J. Xu and S. Funk and T. Ward and C. E. Overton and L. A. Meyers and S. Abbott},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.21.26363521},
url = {https://www.medrxiv.org/content/10.64898/2026.09.21.26363521v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A within-host-informed event-time framework linking epidemiological delay distributions
AU - N. Jamieson
AU - S. W. Park
AU - K. Ainslie
AU - J. Xu
AU - S. Funk
AU - T. Ward
AU - C. E. Overton
AU - L. A. Meyers
AU - S. Abbott
PY - 2026
DA - 2026/09/21
PB - medRxiv
DO - 10.64898/2026.09.21.26363521
UR - https://www.medrxiv.org/content/10.64898/2026.09.21.26363521v1
AB - 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. Author summaryFor many infections that spread directly from person to person, the timing of infection, infectiousness, symptom onset, and passing the infection to someone else is central to understanding how disease spreads. These delays are often estimated separately using statistical distributions, even though they are biologically connected. For example, the amount of pathogen in the body can influence when someone becomes infectious, when symptoms appear, and when transmission is most likely. We developed a modeling framework that links these timings through a shared pathogen growth process. In the model, becoming infectious, developing symptoms, and transmitting infection occur as chance events whose rates change with pathogen load. We tested the framework using simulations and data from recent SARS-CoV-2 and mpox epidemics, showing that it outperformed conventional statistical approaches and can incorporate pathogenload data into delay estimation, while providing new estimates of the generation intervals of recent COVID-19 variants and mpox. More broadly, the framework provides a way to combine epidemiological data with biological information, helping researchers understand how changes in within-host (WH) pathogen dynamics influence transmission and improving our ability to predict and control infectious disease outbreaks.
ER -