Predicting the Growth of in Oysters under Varying Ambient Temperature.

Temperature is a critical factor that influences the proliferation of pathogens in hosts. One example of this is the human pathogen () in oysters. Here, a continuous time model was developed for predicting the growth of in oysters under varying ambient temperature. The model was fit and evaluated against data from previous experiments. Once evaluated, the dynamics in oysters were estimated at different post-harvest varying temperature scenarios affected by water and air temperature and different ice treatment timing. The model performed adequately under varying temperature, reflecting that (i) increasing temperature, particularly in hot summers, favors a rapid growth in oysters, resulting in a very high risk of gastroenteritis in humans after consumption of a serving of raw oysters, (ii) pathogen inactivation due to day/night oscillations and, more evidently, due to ice treatments, and (iii) ice treatment is much more effective, limiting the risk of illness when applied immediately onboard compared to dockside. The model resulted in being a promising tool for improving the understanding of the -oyster system and supporting studies on the public health impact of pathogenic associated with raw oyster consumption. Although robust validation of the model predictions is needed, the initial results and evaluation showed the potential of the model to be easily modified to match similar systems where the temperature is a critical factor shaping the proliferation of pathogens in hosts.