Evidence to Action: Use of Predictive Models for Beach Water Postings
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1 Evidence to Action: Use of Predictive Models for Beach Water Postings Canadian Society for Epidemiology and Biostatistics Caitlyn Paget, June 4 th 2015
2 Goal is to improve program delivery Can we improve the accuracy of our beach postings by predicting the water quality based on beach inspection data? As applied research, project had some constraints: Using an administrative dataset, limited to pre-existing variables Follow modeling methods suggested by the USEPA Linear regression with log-transformed E.coli as response variable Using Virtual Beach, a customized regression software
3 OPHS mandates weekly inspections 100 E.coli / 100ml > 100 E.coli / 100ml Beach Open Beach Posted
4 E. coli concentration [count / 100mL water] E.coli spikes are often transient Wk of 10-Jun Wk of 17-Jun Wk of 24-Jun Wk of 01-Jul Wk of 08-Jul Wk of 15-Jul Wk of 22-Jul Wk of 29-Jul Wk of 05-Aug Wk of 12-Aug Wk of 19-Aug Wk of 26-Aug
5 York has a relatively rich data set Daily beach inspections 5 or 6 water samples per beach Record environmental conditions Weather data from two stations Improvements over time: 2006: Standardized observations 2008: Purchased turbidometers 2011: Enhanced sampling frequency 2012: Purchased UV & wind meters Expanded enhanced sampling
6 Best subsets modeling technique Explored variable transformation Decision threshold of at least 40% improvement in Pearson coefficient Reduced set of variables under consideration Generated sample models using repeated genetic algorithms Compared model accuracy at different E.coli thresholds Generated candidate models Tried all variable combinations up to a maximum of 10 Optimized for predicted residual sum of squares Cross-validation for final model selection Random cross-validation: 1000 iterations setting aside 20-25% of data Simulated results for each beach season
7 Build a specific model for each beach
8 Overall pilot results are positive Beach 2012 Results 2013 Results 2014 Results #1 Significantly better on all measures Moderately better on all measures Moderately better on all measures #2 Fewer missed postings Same accuracy, more missed postings Fewer unnecessary postings #3 Fewer missed postings No difference Moderately better on all measures #4 No difference #5 No difference Fewer unnecessary postings More missed postings, fewer unnecessary ones #6 Fewer missed postings, more unnecessary ones Fewer missed postings
9 Model updates part of ongoing work All new models No changes at midseason check-in Updated either model or betas based on prior model performance No changes at midseason check-in 2014 Updated models for 4 beaches Beach Model Update compared to Betas Update Compared updated models with background updated beta models Updated model again midseason for 1 beach, improved performance #1 #2 #4 Significantly better on all measures Fewer missed postings, more unnecessary ones Better accuracy, fewer unnecessary postings #5 No difference
10 Complex program to communicate Predictive modeling is more challenging for risk communication Persistence system is more intuitive Lab results are gold standard, except for the 24 hour delay in results Ideally communication is simple No changes needed to the Postings Signs
11 Success leads to more work The pilot showed that it is possible to improve the accuracy of beach inspection data, using beach-inspection-based predictive models. However, we d like to see better consistency in model performance for all years & beaches Improve modeling methods: perform cross-validation for all possible variable combinations to determine candidate models based on better performance simulations. (Requires leaving Virtual Beach software.) Continue to improve data quality.
12 Acknowledgements Debeka Navaranjan, Epidemiologist Bernie Mayer, Safe Water Manager Beach Water Public Health Inspector Students Questions?
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