You read that right- Dr. Zhiqiang Deng and his research team recently became the first scientists in the world to predict an oyster norovirus outbreak before it actually occurred. And they used data from NASA satellites to do it. Deng is an associate professor of Water Resources and Coastal Engineering in the Department of Civil and Environmental Engineering at Louisiana State University; his team’s research is a collaborative effort with the Louisiana Department of Health and Hospitals (LDHH).
Oysters are pretty important in Louisiana. Beyond simply eating them, the Gulf Coast region has more than 30 oyster growing areas, and in 2010 was responsible for more than half of the national total of oysters harvested (that total being 15.5 million pounds), making oysters economically significant to the region. Unfortunately, oysters are also a common vector for transmitting food borne illness, including norovirus. Since norovirus is also readily transmissible person-to-person, it’s not always necessary to eat a contaminated oyster, just to have been in contact with someone who did. When outbreaks occur, oyster harvest areas may be closed, sometimes for long periods of time. This has significant economic impacts on the molluscan shellfish industry, not to mention the effects on people who get sick. Currently, monthly water sampling is used to monitor oyster-harvesting areas and thereby assess potential health impacts. However, this method is slow to yield results, and in many cases people are already sick before an area is closed. Thus, current methods are generally unable to prevent outbreaks, in addition to being slow and costly.
Enter Dr. Deng and team. His research group has developed a model for predicting oyster norovirus outbreaks, which relies on assessment of water quality and of the levels of bacteria called fecal coliforms (these come from poop). The team uses data collected by NASA satellites, such as water temperature, to predict bacteria levels and other conditions that are ripe for norovirus outbreaks (for example, Deng notes that outbreaks often occur a certain number of days after very low tides in cold weather). The model allows researchers to predict in advance when the probability of an outbreak is especially high, which means management groups such as LDHH can make decisions to close harvest areas ahead of time. The best part? So far it works- Dr. Deng and team correctly predicted the most recent outbreak in the Cameron Parish Oyster Harvesting Area an impressive 16 days before it happened. Deng hopes to combine his model with NASA data and a publically accessible website that will allow oyster harvesters to make informed decisions about which locations are safe to use.
Additional information at LSU Research News.