Unravelling key drivers of Ostreid herpesvirus type 1 (OsHV- 1) transmission dynamics in Pacific oysters through a data-driven epidemiological modeling approach

Over the past few years, methodological advances have driven major progress in epidemiological modelling tools, improving our ability to understand pathogen dynamics and inform management strategies. However, these powerful approaches remain underused in marine mollusc health, despite their well-recognized potential to assess the impact of pathogens that threaten the long-term viability of the industry. This is notably the case for Ostreid herpesvirus type 1 (OsHV-1), a virus associated with recurrent mass mortalities of Pacific oyster spat worldwide. These recurring outbreaks underscore important gaps in our understanding of its transmission dynamics and the strategies required to mitigate epizootic events. To bridge this gap, we developed a stochastic compartmental epidemiological model that extends the classical SEIR framework by incorporating an environmental viral compartment and distinguishing between oysters that survive infection and those that succumb to it. Model parameters were estimated using targeted experimental data and integrated into stochastic simulations, enabling the model to reproduce the overall dynamics of the observed mortality kinetics and thereby supporting its validity. Remaining discrepancies were then addressed using an Approximate Bayesian Computation approach to refine parameter estimates and improve model accuracy. Additionally, sensitivity analysis identified viral shedding rates as the main drivers of epidemic dynamics. Through this integrative framework, we provide new insights into OsHV-1 transmission patterns and establish a foundation for future spatial modelling aimed at supporting disease management in oyster farming.