Actionable public health information from a temporal milwaukee beach water quality database

Public health authorities post advisories or close beaches based on the presence of indicator bacteria (e.g. E. coli) that may suggest the presence of additional pathogenic species. The evidence supporting issuance of a public notice may be confounded by sample quality, methodology and timeliness of analysis. Timely E. coli counts, coupled with environment-based predictive modeling of E. coli trends at beaches may mitigate these complications and thus improve public health decisions. With the University of Wisconsin-Milwaukee, Zilber School of Public Health joined the Milwaukee Health Department (MHD) to devise and build a data Extract, Integrate, Translate and Load (EITL) process and database to support E. coli predictive modeling at Milwaukee beaches. Lake Michigan water samples were collected from three popular beaches, June to August 2012. Levels of E. coli were assessed by Colilert-18 assays and qPCR. EPA guided survey data were recorded for each beach. Our EITL quality assurance steps produced a robust database supporting predictive modeling. Retrospective analysis was conducted by preparing collected data in conjunction with automated satellite data obtained from the Environmental Data Discovery and Transformation service offered by the United States Geological Survey. Transformed data was input into Virtual Beach (VB), a software package for developing multiple linear regression models to predict pathogen indicator levels at recreational beaches. Combinations of four-variable models were investigated, and overlapping informative variables were selected for generation of a five-variable model demonstrating 83% predictive sensitivity to incidences of E. coli threshold (EPA regulatory standard: 235 most probable number per 100 mL freshwater). Higher order [six- and seven-variable] models were also investigated but did not improve accuracy. This work produced an automated EITL web interface delivering accurate and timely actionable public health information.