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

Authors: Anton Kvit*, Johns Hopkins Bloomberg School of Public Health, John Jacobs, Cooperative Oxford Lab, National Oceanic and Atmospheric Administration, Benjamin Davis, Johns Hopkins Bloomberg School of Public Health, Frank Curierro, Johns Hopkins Bloomberg School of Public Health
Topics: Coastal and Marine, Medical and Health Geography
Keywords: Vibrio, Cluster Detection, Chesapeake Bay
Session Type: Poster
Day: 4/4/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded

Vibrio parahaemolyticus (Vp) is a naturally-occurring bacterium found in estuaries such as the Chesapeake Bay that can cause vibriosis, a foodborne illness, in humans. The exposure to the bacterium commonly occurs from eating raw or undercooked shellfish such as oysters, which accumulate Vp via filter feeding. Tracking the spatial and temporal distribution and clustering of Vp in the Chesapeake Bay, which varies in part due to water temperature, salinity, and other environmental variables is important to help identify areas of highest risk and to effectively reduce the burden of vibriosis.

SaTScan cluster detection software was used to identify purely spatial and spatiotemporal clusters of high Vp abundance in the Chesapeake Bay between 2007 and 2010. Due to the complex nature of the Chesapeake Bay shoreline and the fact that Vp travels via water rather than over land, non-Euclidean water distances, instead of the commonly used Euclidean distances, were also considered for cluster detection, and were determined to be the more appropriate approach. In order to identify how clusters changed after controlling for water temperature, salinity, clarity, and dissolved oxygen, residuals from univariate and multivariate regression models adjusting for these parameters were used for cluster detection in place of Vp abundance measures. Most clusters tended to decrease in space, time, and/or significance after adjusting for these covariates, suggesting their importance in explaining the clusters. Clusters that remained after adjustment suggest areas for further study and intervention.

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