2023 journal article
Predicting the Growth of <i>Vibrio parahaemolyticus</i> in Oysters under Varying Ambient Temperature
Microorganisms.
TL;DR:
A continuous time model was developed for predicting the growth of Vibrio parahaemolyticus in oysters under varying ambient temperature and 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.
(via Semantic Scholar)
open access via doi.org (publisher)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: ORCID
Added: May 4, 2023
Temperature is a critical factor that influences the proliferation of pathogens in hosts. One example of this is the human pathogen Vibrio parahaemolyticus (V. parahaemolyticus) in oysters. Here, a continuous time model was developed for predicting the growth of Vibrio parahaemolyticus in oysters under varying ambient temperature. The model was fit and evaluated against data from previous experiments. Once evaluated, the V. parahaemolyticus 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 V. parahaemolyticus 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 V. parahaemolyticus–oyster system and supporting studies on the public health impact of pathogenic V. parahaemolyticus 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.