A Reliability-Aware Hotspot Scoring Framework for Interpreting Pathogen and Chemical Signals in Wastewater Monitoring

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Wastewater pathogen and chemical records differ in measurement meaning, sampling frequency, analytical context, and data support, which complicates record-level screening and interpretation. This study developed and internally examined Hotspot Finder, a transparent Python-based framework that represents each eligible record using Level, Trend, Jump, and Reliability scores. Pathogen Level and Trend were mapped from official Government of Canada categories, whereas chemical scores were calculated within compatible analyte, wastewater treatment plant, sample type or stream, and analytical-unit series. Jump represented categorical change from the most recent earlier valid observation, and Reliability represented rule-based evidence support. The final Hotspot score was the arithmetic mean of the four components. The most-recent outputs contained 104 pathogen records and 24,142 eligible chemical records, with mean scores of 61.57 and 53.73, respectively. A Montreal Influenza B record dated 1 March 2026 scored 90.00, while a PFBS influent record dated 10 April 2024 scored 76.25. Sensitivity analysis showed smaller effects from changing chemical Trend-history length than from altering percentage-change categories, Reliability rules, or alert boundaries. The framework provided reproducible calculations, separate pathogen and chemical rankings, and record-level traceability. It supports transparent screening but does not establish clinical, epidemiological, toxicological, regulatory, predictive, or end-user validity.