Coliphage are viruses that infect Escherichia coli (E. coli) and may indicate the presence of enteric viral pathogens in recreational waters. There is an increasing interest in using these viruses for water quality monitoring and forecasting; however, the ability to use statistical models to predict the concentrations of coliphage, as often done for cultured fecal indicator bacteria (FIB) such as enterococci and E. coli, has not been widely assessed. The same can be said for FIB genetic markers measured using quantitative polymerase chain reaction (qPCR) methods. Here we institute least-angle regression (LARS) modeling of previously published concentrations of cultured FIB (E. coli, enterococci) and coliphage (F+, somatic), along with newly reported genetic concentrations measured via qPCR for E. coli, enterococci, and general Bacteroidales. We develop site-specific models from measures taken at three beach sites on the Great Lakes (Grant Park, South Milwaukee, WI; Edgewater Beach, Cleveland, OH; Washington Park, Michigan City, IN) to investigate the efficacy of a statistical predictive modeling approach. Microbial indicator concentrations were measured in composite water samples collected five days per week over a beach season (∼15 weeks). Model predictive performance (cross-validated standardized root mean squared error of prediction [SRMSEP] and R²_PRED) were examined for seven microbial indicators (using log₁₀ concentrations) and water/beach parameters collected concurrently with water samples. Highest predictive performance was seen for qPCR-based enterococci and Bacteroidales models, with F+ coliphage consistently yielding poor performing models. Influential covariates varied by microbial indicator and site. Antecedent rainfall, bird abundance, wave height, and wind speed/direction were most influential across all models. Findings suggest that some fecal indicators may be more suitable for water quality forecasting than others at Great Lakes beaches.

大肠杆菌噬菌体是感染大肠杆菌( E. coli ) 的病毒，可能表明娱乐水域中存在肠道病毒病原体。人们越来越有兴趣利用这些病毒进行水质监测和预测；然而，使用统计模型预测大肠杆菌噬菌体浓度的能力（通常用于培养的粪便指示菌（FIB），如肠球菌和大肠杆菌）尚未得到广泛评估。对于使用定量聚合酶链式反应 (qPCR) 方法测量的 FIB 遗传标记来说也是如此。在这里，我们对先前发表的培养 FIB（大肠杆菌、肠球菌）和大肠杆菌噬菌体（F+、体细胞）浓度以及新报告的通过 qPCR 测量的大肠杆菌、肠球菌和大肠杆菌的遗传浓度建立了最小角回归 (LARS) 模型。一般拟杆菌目。我们根据五大湖三个海滩地点（威斯康星州南密尔沃基格兰特公园；俄亥俄州克利夫兰埃奇沃特海滩；印第安纳州密歇根城华盛顿公园）采取的措施开发了特定地点模型，以研究统计预测模型的有效性方法。在海滩季节（约 15 周）每周收集五天的复合水样中测量微生物指示剂浓度。_{针对与水样同时收集的七种微生物指标（使用 log 10}浓度）和水/海滩参数，检查了模型预测性能（交叉验证的标准化预测均方根误差 [SRMSEP] 和 R ² _{PRED ）。}基于 qPCR 的肠球菌和拟杆菌模型的预测性能最高，而 F+ 大肠杆菌噬菌体始终产生性能较差的模型。有影响的协变量因微生物指标和位点而异。前期降雨量、鸟类数量、波高和风速/风向对所有模型影响最大。研究结果表明，五大湖海滩的一些粪便指标可能比其他指标更适合水质预测。