Adjusted, non-Euclidean cluster detection of Vibrio parahaemolyticus in the Chesapeake Bay, USA

  • Anton Kvit Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
  • Benjamin Davis Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
  • John Jacobs Cooperative Oxford Lab, National Oceanic and Atmospheric Administration, Oxford, MD, United States.
  • Frank C. Curriero | fcurriero@jhu.edu Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.

Abstract

Vibrio parahaemolyticus (V. parahaemolyticus) is a naturallyoccurring bacterium found in estuaries, such as the Chesapeake Bay (USA), that can cause vibriosis, a food - and waterborne illness, in humans. Tracking the spatial and temporal distribution of V. parahaemolyticus in the Chesapeake Bay, which varies in part due to water temperature, salinity, and other environmental variables, can help identify areas and time periods of high risk. These observations can support interventions used to reduce the burden of vibriosis. Spatial and spatiotemporal clusters of high V. parahaemolyticus abundance were identified among surface water samples in the Chesapeake Bay between 2007 and 2010. While Euclidean distances between geographic points in spatial analyses are often used for cluster detection, non-Euclidean distances should be considered for cluster detection due to the complex nature of the Chesapeake Bay shoreline. Comparison of both methods consistently showed the non-Euclidean cluster detection providing unique and more reasonable clusters than the Euclidean approach. Residuals from univariate and multivariate models were used to identify how clusters changed after controlling for environmental variables. Most clusters tended to decrease in space, time, or significance after adjustment, suggesting these covariates contributed to the original formation of the clusters and as such are useful observation tools for vibriosis risk managers. Clusters that remained after adjustment suggest areas for further study and intervention. These findings reinforce the importance of using non-Euclidean distances when tracking the spatiotemporal variation of V. parahaemolyticus as well as the benefits of cluster detection methods for V. parahaemolyticus risk management in estuaries.

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Published
2019-11-06
Section
Original Articles
Keywords:
Vibrio parahaemolyticus, Cluster detection, Chesapeake Bay, Non-Euclidean, Spatiotemporal
Statistics
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PDF: 61
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How to Cite
Kvit, A., Davis, B., Jacobs, J., & Curriero, F. (2019). Adjusted, non-Euclidean cluster detection of Vibrio parahaemolyticus in the Chesapeake Bay, USA. Geospatial Health, 14(2). https://doi.org/10.4081/gh.2019.783