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|Title: ||Probabilistic quantitative microbial risk assessment model of norovirus fromwastewater irrigated vegetables in Ghana using genome copies and fecal indicator ratio conversion for estimating exposure dose|
|Authors: ||Owusu-Ansah, Emmanuel de-Graft Johnson|
Amponsah, Samuel K.
Abaidoo, Robert C.
Disability Adjusted Life Years
|Issue Date: ||2017|
|Publisher: ||Elsevier B.V|
|Citation: ||Elsevier B.V, Science of the Total Environment 601–602 (2017) 1712–1719|
|Abstract: ||The need to replace the commonly applied fecal indicator conversions ratio (an assumption of 1:10−5 virus to
fecal indicator organism) in Quantitative Microbial Risk Assessment (QMRA) with models based on quantitative
data on the virus of interest has gained prominence due to the different physical and environmental factors that
might influence the reliability of using indicator organisms in microbial risk assessment. The challenges facing
analytical studies on virus enumeration (genome copies or particles) have contributed to the already existing
lack of data in QMRA modelling. This study attempts to fit a QMRA model to genome copies of norovirus data.
The model estimates the risk of norovirus infection from the intake of vegetables irrigated with wastewater
fromdifferent sources. The resultswere compared to the results of a corresponding model using the fecal indicator
conversion ratio to estimate the norovirus count. In all scenarios of using differentwater sources, the application
of the fecal indicator conversion ratio underestimated the norovirus disease burden, measured by the
Disability Adjusted Life Years (DALYs), when compared to results using the genome copies norovirus data. In
some cases the difference was N2 orders of magnitude. All scenarios using genome copies met the 10−4 DALY
per person per year for consumption of vegetables irrigated with wastewater, although these results are considered
to be highly conservative risk estimates.|
|Description: ||An article published by Elsevier B.V and also available at .doi.org/10.1016/j.scitotenv.2017.05.168|
|Appears in Collections:||College of Science|
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