Modeling the Transmission of Vibrio aestuarianus in Pacific Oysters Using Experimental Infection Data
|Author(s)||Lupo Coralie1, Travers Marie-Agnes1, Tourbiez Delphine1, Barthélémy Clement1, Beaunée Gaël2, Ezanno Pauline2|
|Affiliation(s)||1 : Laboratoire de Génétique et Pathologie des Mollusques Marins, SG2M-LGPMM, Ifremer, La Tremblade, France
2 : BIOEPAR, INRA, Oniris, Nantes, France
|Source||Frontiers In Veterinary Science (2297-1769) (Frontiers Media SA), 2019-05 , Vol. 6 , N. 142 , P. 15p.|
|Keyword(s)||marine epidemiology, parameter estimation, compartmental model, ABC method, global sensitivity analysis, basic reproduction number R-0, Crassostrea gigas, oyster mortality|
Vibrio aestuarianus is a bacterium related to mortality outbreaks in Pacific oysters, Crassostrea gigas, in France, Ireland, and Scotland since 2011. Knowledge about its transmission dynamics is still lacking, impairing guidance to prevent and control the related disease spread. Mathematical modeling is a relevant approach to better understand the determinants of a disease and predict its dynamics in imperfectly observed pathosystems. We developed here the first marine epidemiological model to estimate the key parameters of V. aestuarianus infection at a local scale in a small and closed oyster population under controlled laboratory conditions. Using a compartmental model accounting for free-living bacteria in seawater, we predicted the infection dynamics using dedicated and model-driven collected laboratory experimental transmission data. We estimated parameters and showed that waterborne transmission of V. aestuarianus is possible under experimental conditions, with a basic reproduction number R0 of 2.88 (95% CI: 1.86; 3.35), and a generation time of 5.5 days. Our results highlighted a bacterial dose–dependent transmission of vibriosis at local scale. Global sensitivity analyses indicated that the bacteria shedding rate, the concentration of bacteria in seawater that yields a 50% chance of catching the infection, and the initial bacterial exposure dose W0 were three critical parameters explaining most of the variation in the selected model outputs related to disease spread, i.e., R0, the maximum prevalence, oyster survival curve, and bacteria concentration in seawater. Prevention and control should target the exposure of oysters to bacterial concentration in seawater. This combined laboratory–modeling approach enabled us to maximize the use of information obtained through experiments. The identified key epidemiological parameters should be better refined by further dedicated laboratory experiments. These results revealed the importance of multidisciplinary approaches to gain consistent insights into the marine epidemiology of oyster diseases.