季节性自回归差分移动平均模型在牡蛎中诺如病毒检出率预测上的应用

目的 基于季节性自回归差分移动平均(ARIMA)模型分析并预测上海市售牡蛎中诺如病毒(NoV)的检出率,为水产品中NoV的污染规律提供参考.方法 2016年6月-2019年11月,从上海芦潮港海鲜市场定期采购牡蛎样品共531只,通过巢式聚合酶链式反应(Nest-PCR),对其进行了 NoV检测,按季度分析检出率.采用季...

Application of seasonal ARIMA model in prediction of detection rate of norovirus in oyster

Objective The seasonal autoregressive integrated moving average (ARIMA) model was used to predict the detection rate of norovirus in oysters sold in Shanghai, which provided a reference for the prevalence of norovirus in aquatic products. The seasonal autoregressive integrated moving average (ARIMA) model is used to predict the detection rate of norovirus in oysters sold in Shanghai, which provides a reference for the prevalence of norovirus in aquatic products.Methods Oyster samples were regularly purchased from the Shanghai Luchaogang seafood market. A total of 531 oyster samples were tested for norovirus by nest-polymerase chain reaction(Nest-PCR), and the positive detection rate was calculated every quarter. The seasonal ARIMA model was used to fit the norovirus detection rate data in oysters from June 2016 to November 2019 to construct the model. After data stabilization, model selection and fitting and model diagnosis, the optimal model was obtained and the optimal model was used to predict the detection rate of norovirus in oysters in 2020. Regularly purchased oyster samples from the Shanghai Luchaogang seafood market. A total of 531 oyster samples were tested for norovirus by nested-PCR, and the positive detection rate was calculated every quarter. The seasonal ARIMA model was used to fit the norovirus detection rate data in oysters from June 2016 to November 2019 to construct the model. After data stabilization, model selection and fitting, and model diagnosis, the optimal model is obtained, and the optimal model is used to predict the detection rate of norovirus in oysters in 2020. Results The seasonal ARIMA (0,1, 1) (0,1, 0)₄ was the optimal model. Akaike''s information criterion and the finite corrections (AICc) (58.70) was the smallest. The residual error was a white noise sequence by Ljung-Box test. The trend of norovirus positive rate in oysters fitted by the model was basically consistent with the trend of actual detection rate, the mean absolute error (MAE) was 4.85 and the mean absolute percentage error (MAPE) was 30.25. The positive detection rates of norovirus in oysters predicted by the optimal model in the next four quarters were 31.89%, 12.80%, 9.47%, and 6.14%, respectively. The seasonal ARIMA (0,1, 1) (0,1, 0) ₄ is the optimal model. Akaike''s information criterion and the finite corrections (AICc) (58.70) is the smallest. The residual error is a white noise sequence by Ljung-Box test. The trend of norovirus positive rate in oysters fitted by the model is basically consistent with the trend of actual detection rate, the mean absolute error (MAE) is 4.85 and the mean absolute percentage error (MAPE) is 30.25. The positive detection rates of norovirus in oysters predicted by the optimal model in the next four quarters were 31.89%, 12.80%, 9.47%, and 6.14%, respectively.Conclusion The seasonal ARIMA model (0,1, 1) (0,1, 0)₄ can fit the trend of positive detection rate of norovirus in oysters. This model has certain significance for the risk assessment of aquatic products such as oysters contaminated by norovirus. It also has certain significance for the prevention and control of the norovirus epidemic. The seasonal ARIMA model (0,1, 1) (0,1, 0) ₄ can fit the trend of positive detection rate of norovirus in oysters. This model has certain significance for the risk assessment of aquatic products such as oysters contaminated by norovirus. It also has certain significance for the prevention and control of the norovirus epidemic.