Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States.
Water Res. 2018 Jan 1;128:20-37. doi: 10.1016/j.watres.2017.10.032. Epub 2017 Nov 1.
Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was utilized to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53% and the true negative rate (specificity) of 88.82%, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry.
牡蛎诺如病毒爆发对全球人类健康和海鲜产业构成的风险日益增加，但爆发的确切原因很少被确定，因此极不可能降低风险。本文提出了一种基于遗传编程（GP）的方法，用于识别牡蛎诺如病毒爆发的主要原因，并预测牡蛎诺如病毒爆发，以降低风险。就主要原因而言，研究发现，牡蛎诺如病毒爆发受到低太阳辐射、低水温、低水位（水位高于基准点的高度）、低盐度、强降雨和强离岸风等先前环境条件累积效应的控制。在 GP 方法框架内，使用随机森林（RF）和二元逻辑回归（BLR）方法确定了这六个环境变量。在预测诺如病毒爆发方面，使用六个环境变量和不同时间滞后的变量的各种组合，开发了一种基于风险的 GP 模型。局部和全局敏感性分析的结果表明，水位、温度和太阳辐射是迄今为止对牡蛎诺如病毒爆发最重要的三个环境预测因子，尽管其他变量也很重要。具体来说，极低的温度和水位显著增加了诺如病毒爆发的风险，而高太阳辐射则显著降低了风险，这表明低温和水位与诺如病毒的来源有关，而太阳辐射是诺如病毒的主要汇。利用 GP 模型对墨西哥湾北部沿海地区的牡蛎诺如病毒爆发的每日风险进行了回溯预测。每日回溯预测结果表明，该 GP 模型能够回溯预测 2002 年 1 月至 2014 年 6 月在墨西哥湾发生的所有历史牡蛎诺如病毒爆发事件，在 12.5 年期间仅出现两次假阳性爆发事件。GP 模型的性能特征是接收者操作特征曲线下的面积为 0.86，真阳性率（敏感性）为 78.53%，真阴性率（特异性）为 88.82%，表明该模型的有效性。这些发现和结果为牡蛎诺如病毒爆发在来源、汇、原因和预测因子方面提供了新的见解。GP 模型为预测潜在的牡蛎诺如病毒爆发并实施管理干预措施提供了一种高效、有效的工具，以预防或至少降低人类健康和海鲜产业的诺如病毒风险。
