Predictive aquatic bacterial models are promising tools as early warning systems to prevent from exposure to waterborne pathogens in beaches. Using historical environmental and water quality data collected from Georgia’s Coastal Resources Division (CRD) and the University of Georgia, this project lays the groundwork for a predictive model to fill in the data gaps on the coast and utilize existing routine monitoring data to predict future exposures. This study investigates rainfall as a predictor of fecal indicator bacteria at Tybee Island, one of the most popular beaches in Georgia.

Weekly collected samples collected from 2004 to 2023 across five beaches at Tybee Island were analyzed for relationship between three-day rainfall cumulative total and fecal indicator bacteria levels. Pearson correlations ranged from 0.098 to 0.004, and all results were significant (p<0.05, n=4750). Sites with lowest correlation were more influenced by either point or non-point source pollution, while higher correlations pointed to less populated beaches where rainfall might have carried runoff to the beach.

The results of this study informs impact of rainfall as a predictor for future predictive models. Additional analyses are ongoing to include other environmental factors such as tides, winds, temperature, and salinity. Collaboration with the CRD and health department will allow for co-developing an effective health promotion strategy for beachgoers and better protect their health in the future.