GC11H-1117
Modeling and Mapping Oyster Norovirus Outbreak Risks in Gulf of Mexico Using NASA MODIS Aqua Data

Monday, 14 December 2015
Poster Hall (Moscone South)
Zhiqiang Deng, Louisiana State University, Baton Rouge, LA, United States
Abstract:
Norovirus is a highly infectious virus and the leading cause of foodborne disease outbreaks such as oyster norovirus outbreaks. Currently, there is no vaccine to prevent norovirus infection and no drug to treat it. This paper presents an integrated modeling and mapping framework for predicting the risk of norovirus outbreaks in oyster harvesting waters in the Northern Gulf of Mexico coast. The framework involves (1) the construction of three novel remote sensing algorithms for the retrieval of sea surface salinity, sea surface temperature, and gage height (tide level) using NASA MODIS Aqua data; (2) the development of probability-based Artificial Neural Network (ANN) model for the prediction of oyster norovirus outbreak risk, and (3) the application of the Local Indicators of Spatial Association (LISA) for mapping norovirus outbreak risks in oyster harvesting areas in the Northern Gulf of Mexico using the remotely sensed NASA data, retrieved data from the three remote sensing algorithms, and the ANN model predictions. The three remote sensing algorithms are able to correctly retrieve 94.1% of sea surface salinity, 94.0% of sea surface temperature, and 77.8% of gage height observed along the US coast, including the Pacific coast, the Gulf of Mexico coast, and the Atlantic coast. The gage height, temperature, and salinity are the three most important explanatory variables of the ANN model in terms of spatially distributed input variables. The ANN model is capable of hindcasting/predicting all oyster norovirus outbreaks occurred in oyster growing areas along the Gulf of Mexico coast where environmental data are available. The integrated modeling and mapping framework makes it possible to map daily risks of norovirus outbreaks in all oyster harvesting waters and particularly the oyster growing areas where no in-situ environmental data are available, greatly improving the safety of seafood and reducing outbreaks of foodborne disease.