| DOI: | 10.1016/j.watres.2017.10.032
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| Scopus记录号: | 2-s2.0-85032031052
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| 论文题名: | Development of genetic programming-based model for predicting oyster norovirus outbreak risks |
| 作者: | Chenar S.S.; Deng Z.
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| 刊名: | Water Research
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| ISSN: | 431354
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| 出版年: | 2018
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| 卷: | 128 | | 起始页码: | 20
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| 结束页码: | 37
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| 语种: | 英语
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| 英文关键词: | Genetic programming
; Oyster norovirus outbreaks
; Predictive model
; Sensitivity analysis
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| Scopus关键词: | Aquaculture
; Decision trees
; Forecasting
; Gages
; Genetic algorithms
; Health risks
; Molluscs
; Radiation effects
; Rain
; Sensitivity analysis
; Shellfish
; Solar radiation
; Temperature
; Binary logistic regression
; Environmental conditions
; Environmental variables
; Global sensitivity analysis
; Management interventions
; Norovirus
; Predictive modeling
; Receiver operating characteristic curves
; Genetic programming
; rain
; water
; environmental conditions
; genetic algorithm
; health risk
; hindcasting
; low temperature
; performance assessment
; prediction
; public health
; risk assessment
; seafood
; solar radiation
; virus
; Article
; epidemic
; genetic analysis
; genetic programming
; Gulf of Mexico
; height
; low temperature
; nonhuman
; Norovirus
; oyster
; prediction
; priority journal
; random forest
; receiver operating characteristic
; risk reduction
; salinity
; seashore
; solar radiation
; water temperature
; wind
; animal
; calicivirus infection
; chemistry
; gastroenteritis
; genetics
; human
; Norovirus
; oyster
; pathogenicity
; risk
; risk factor
; statistical model
; temperature
; virology
; Atlantic Ocean
; Gulf of Mexico
; Norovirus
; Oyster norovirus
; Animals
; Caliciviridae Infections
; Disease Outbreaks
; Gastroenteritis
; Gulf of Mexico
; Humans
; Logistic Models
; Norovirus
; Ostreidae
; Risk
; Risk Factors
; Temperature
; Water
|
| 英文摘要: | 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. © 2017 Elsevier Ltd |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/113160
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| Appears in Collections: | 气候减缓与适应
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作者单位: | Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States
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| Recommended Citation: | |
Chenar S.S.,Deng Z.. Development of genetic programming-based model for predicting oyster norovirus outbreak risks[J]. Water Research,2018-01-01,128
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