Journal
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
Volume 16, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/ijerph16224341
Keywords
Vibrio parahaemolyticus; seasonality; seafood illness; forecasting; climate change
Funding
- National Science Foundation EPSCoR [IIA-1330641]
- USDA National Institute of Food and Agriculture [NH00609, NH00625]
- National Oceanic and Atmospheric Administration College Sea Grant program
- National Oceanic and Atmospheric Administration New Hampshire Sea Grant program [R/CE-137, R/SSS-2, R/HCE-3]
- NSF IRES Track I: Collaborative Research: U.S.-Indonesian Research Experience for Students on Sustainable Adaptation of Coastal Areas to Environmental Change [1826939]
- Office Of The Director
- Office Of Internatl Science &Engineering [1826939] Funding Source: National Science Foundation
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Seafood-borne Vibrio parahaemolyticus illness is a global public health issue facing resource managers and the seafood industry. The recent increase in shellfish-borne illnesses in the Northeast United States has resulted in the application of intensive management practices based on a limited understanding of when and where risks are present. We aim to determine the contribution of factors that affect V. parahaemolyticus concentrations in oysters (Crassostrea virginica) using ten years of surveillance data for environmental and climate conditions in the Great Bay Estuary of New Hampshire from 2007 to 2016. A time series analysis was applied to analyze V. parahaemolyticus concentrations and local environmental predictors and develop predictive models. Whereas many environmental variables correlated with V. parahaemolyticus concentrations, only a few retained significance in capturing trends, seasonality and data variability. The optimal predictive model contained water temperature and pH, photoperiod, and the calendar day of study. The model enabled relatively accurate seasonality-based prediction of V. parahaemolyticus concentrations for 2014-2016 based on the 2007-2013 dataset and captured the increasing trend in extreme values of V. parahaemolyticus concentrations. The developed method enables the informative tracking of V. parahaemolyticus concentrations in coastal ecosystems and presents a useful platform for developing area-specific risk forecasting models.
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