Vibrio parahaemolyticus in the Chesapeake Bay: Operational in situ prediction and forecast models can benefit from inclusion of lagged water quality measurements

Benjamin J.K. Davis, John M. Jacobs, Benjamin Zaitchik, Angelo DePaola, Frank C. Curriero

Research output: Contribution to journalArticle

Abstract

Vibrio parahaemolyticus is a leading cause of seafood-borne gastroenteritis. Given its natural presence in brackish waters, there is a need to develop operational forecast models that can sufficiently predict the bacterium's spatial and temporal variation. This work attempted to develop V. parahaemolyticus prediction models using frequently measured time-indexed and -lagged water quality measures. Models were built using a large data set (n=1,043) of surface water samples from 2007 to 2010 previously analyzed for V. parahaemolyticus in the Chesapeake Bay. Water quality variables were classified as time indexed, 1-month lag, and 2-month lag. Tobit regression models were used to account for V. parahaemolyticus measures below the limit of quantification and to simultaneously estimate the presence and abundance of the bacterium. Models were evaluated using cross-validation and metrics that quantify prediction bias and uncertainty. Presence classification models containing only one type of water quality parameter (e.g., temperature) performed poorly, while models with additional water quality parameters (i.e., salinity, clarity, and dissolved oxygen) performed well. Lagged variable models performed similarly to time-indexed models, and lagged variables occasionally contained a predictive power that was independent of or superior to that of time-indexed variables. Abundance estimation models were less effective, primarily due to a restricted number of samples with abundances above the limit of quantification. These findings indicate that an operational in situ prediction model is attainable but will require a variety of water quality measurements and that lagged measurements will be particularly useful for forecasting. Future work will expand variable selection for prediction models and extend the spatial-temporal extent of predictions by using geostatistical interpolation techniques.

Original languageEnglish (US)
Article numbere01007-19
JournalApplied and environmental microbiology
Volume85
Issue number17
DOIs
StatePublished - Sep 1 2019

Keywords

  • Chesapeake Bay
  • Forecast
  • Prediction
  • Public health
  • Temporal lags
  • Tobit regression
  • Vibrio parahaemolyticus

ASJC Scopus subject areas

  • Biotechnology
  • Food Science
  • Applied Microbiology and Biotechnology
  • Ecology

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