Forecasting Seasonal Vibrio parahaemolyticus Concentrations in New England Shellfish

Meghan A. Hartwick, Erin A. Urquhart, Cheryl A. Whistler, Vaughn S. Cooper, Elena N. Naumova and Stephen H. Jones
Additional contact information
Meghan A. Hartwick: Northeast Center for Vibrio Disease and Ecology, University of New Hampshire, Durham, NH 03824, USA
Erin A. Urquhart: Northeast Center for Vibrio Disease and Ecology, University of New Hampshire, Durham, NH 03824, USA
Cheryl A. Whistler: Northeast Center for Vibrio Disease and Ecology, University of New Hampshire, Durham, NH 03824, USA
Vaughn S. Cooper: Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
Elena N. Naumova: Division of Nutrition Data Sciences, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA
Stephen H. Jones: Northeast Center for Vibrio Disease and Ecology, University of New Hampshire, Durham, NH 03824, USA

IJERPH, 2019, vol. 16, issue 22, 1-24

Abstract: 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.

Keywords: Vibrio parahaemolyticus; seasonality; seafood illness; forecasting; climate change (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/16/22/4341/pdf (application/pdf)
https://www.mdpi.com/1660-4601/16/22/4341/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:16:y:2019:i:22:p:4341-:d:284481

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().