A Bayesian framework to model temperature-dependent transmissible cancer dynamics within soft-shell clam (Mya arenaria) populations

Abstract

Bivalve Transmissible Neoplasia (BTN) is a recently recognized transmissible cancer that spreads among bivalve species worldwide through the transmission of living cancer cells, producing disease dynamics unlike those of typical pathogens. Its marine setting adds a layer of complexity to disease transmission that we account for by modifying standard infectious disease models. Many elements of BTN including transmission, seasonality, and environmental sensitivity are not well understood, which represents a challenge for managing this infectious cancer. In this study, we examined how BTN progresses within Mya arenaria populations and how temperature affects these dynamics. To accomplish this objective, we developed a Bayesian epidemiological model of BTN progression within clam populations. We used this model to quantify transmission and progression rates along with the uncertainties of these estimations. Comparisons among model variants that included different combinations of temperature dependencies indicated that the incorporation of temperature into the model improved its ability to reproduce the patterns observed in survey data. The results further indicate that temperature-dependent progression had the largest impact among the mechanisms evaluated in explaining the seasonal variation present in survey data. However, the results of this study also suggest that there are additional mechanisms, not included in this model, that contribute to the dynamics of BTN infection in clam populations. Overall, this study establishes a quantitative, mechanistic framework for describing the dynamics of BTN in natural clam populations and identifies key areas for future research in both laboratory and field work.