Lupo Coralie, Travers Marie-Agnès, Tourbiez Delphine, Barthélémy Clément Félix, Beaunée Gaël, Ezanno Pauline
Laboratoire de Génétique et Pathologie des Mollusques Marins, SG2M-LGPMM, Ifremer, La Tremblade, France.
BIOEPAR, INRA, Oniris, Nantes, France.
Front Vet Sci. 2019 May 14;6:142. doi: 10.3389/fvets.2019.00142. eCollection 2019.
is a bacterium related to mortality outbreaks in Pacific oysters, , 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 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 is possible under experimental conditions, with a basic reproduction number R 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 W were three critical parameters explaining most of the variation in the selected model outputs related to disease spread, i.e., R, 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.
自2011年以来，它是一种与法国、爱尔兰和苏格兰太平洋牡蛎死亡率爆发相关的细菌。关于其传播动态的知识仍然缺乏，这妨碍了预防和控制相关疾病传播的指导。数学建模是一种相关方法，可更好地了解疾病的决定因素并预测其在观察不完全的病理系统中的动态。我们在此开发了第一个海洋流行病学模型，以在受控实验室条件下估计小型封闭牡蛎种群中局部尺度下感染的关键参数。使用一个考虑海水中自由生活细菌的 compartmental 模型，我们使用专门的、由模型驱动收集的实验室实验传播数据预测感染动态。我们估计了参数，并表明在实验条件下，该细菌的水传播是可能的，基本繁殖数R为2.88（95%置信区间：1.86；3.35），世代时间为5.5天。我们的结果突出了局部尺度下弧菌病的细菌剂量依赖性传播。全局敏感性分析表明，细菌脱落率、导致50%感染机会的海水中细菌浓度以及初始细菌暴露剂量W是解释所选模型输出中与疾病传播相关的大部分变化的三个关键参数，即R、最大患病率、牡蛎生存曲线和海水中细菌浓度。预防和控制应针对牡蛎暴露于海水中细菌浓度的情况。这种实验室 - 建模相结合的方法使我们能够最大限度地利用通过实验获得的信息。确定的关键流行病学参数应通过进一步的专门实验室实验得到更好的完善。这些结果揭示了多学科方法对于一致深入了解牡蛎疾病海洋流行病学的重要性。
