Development of genetic programming-based model for predicting oyster norovirus outbreak risks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{CHENAR:2018:WR,
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author = "Shima Shamkhali Chenar and Zhiqiang Deng",
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title = "Development of genetic programming-based model for
predicting oyster norovirus outbreak risks",
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journal = "Water Research",
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volume = "128",
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pages = "20--37",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Oyster
norovirus outbreaks, Predictive model, Sensitivity
analysis",
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ISSN = "0043-1354",
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DOI = "doi:10.1016/j.watres.2017.10.032",
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URL = "http://www.sciencedirect.com/science/article/pii/S0043135417308692",
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abstract = "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 used 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 78.53percent and
the true negative rate (specificity) of 88.82percent,
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",
- }
Genetic Programming entries for
Shima Shamkhali Chenar
Zhiqiang Deng
Citations