共 50 条
Forecasting Seasonal Vibrio parahaemolyticus Concentrations in New England Shellfish
被引:22
|作者:
Hartwick, Meghan A.
[1
,2
]
Urquhart, Erin A.
[1
,3
]
Whistler, Cheryl A.
[1
,2
]
Cooper, Vaughn S.
[4
]
Naumova, Elena N.
[5
]
Jones, Stephen H.
[1
,2
,3
]
机构:
[1] Univ New Hampshire, Northeast Ctr Vibrio Dis & Ecol, Durham, NH 03824 USA
[2] Univ New Hampshire, Dept Mol Cellular & Biomed Sci, Durham, NH 03824 USA
[3] Univ New Hampshire, Dept Nat Resources & Environm, Durham, NH 03824 USA
[4] Univ Pittsburgh, Sch Med, Dept Microbiol & Mol Genet, Pittsburgh, PA 15261 USA
[5] Tufts Univ, Friedman Sch Nutr Sci & Policy, Div Nutr Data Sci, Boston, MA 02111 USA
基金:
美国国家科学基金会;
美国海洋和大气管理局;
美国食品与农业研究所;
关键词:
Vibrio parahaemolyticus;
seasonality;
seafood illness;
forecasting;
climate change;
CHESAPEAKE BAY OYSTERS;
ENVIRONMENTAL-FACTORS;
PATHOGENIC VIBRIOS;
SOUTHWEST COAST;
NORTH-CAROLINA;
MULTIPLEX PCR;
VULNIFICUS;
ECOLOGY;
TEMPERATURE;
DYNAMICS;
D O I:
10.3390/ijerph16224341
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
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.
引用
收藏
页数:24
相关论文