The overall goal of this project is to demonstrate the feasibility of utilizing NASA Terra and Aqua MODIS data in detection and forecasting of oyster norovirus outbreaks in coastal Louisiana. The proposed strategy is to develop a Detection and Forecasting System (DAFS) for oyster norovirus outbreaks by combining (1) environmental data from NASA Terra/Aqua MODIS sensors and in-situ sensors, (2) bacteriological data from field sampling and laboratory analysis, and (3) modeling efforts. The DAFS consists of (1) a series of retrieval algorithms or water quality models that link NASA MODIS data to water quality indicators controlling norovirus disease outbreaks; (2) an Artificial Neural Network model for detection and forecasting of fecal coliform (norovirus indicator organism); and (3) a hierarchical Bayesian model for detection and forecasting of norovirus disease outbreak risks. The DAFS enables shellfish managers to make two types of decision: daily or detection management (DM) decision (open/close) and forecasting management (FM) decision (classification/reclassification). The DM capability makes it possible to reduce decision-making (open/close) time from current 2-4 months to 1 day. The FM function is able to predict oyster norovirus outbreaks in a probabilistic fashion. The FM function is essential to the classification/reclassification of oyster growing areas and the long-term planning and sustainable management of oysters. This is a major step toward remote sensing assisted detection and forecasting of oyster norovirus outbreaks. Consequently, this project will provide an innovative and practicable application of NASA satellite data in supporting decision-making activities on infectious disease management in the Gulf of Mexico Region and in the nation as well. The proposal is closely relevant to the priority topics of this program: infectious disease (norovirus), emergency preparedness and response (disease detection and forecasting), and environmental health issues (fecal contamination).