Artificial Intelligence Bacteriological Water Quality Forecasting for Healthcare Facility Intake Under Variable Chlorine Test Strip Lot Bias: A Measurement-Error-Aware Evaluation of Healthcare Facility Siting Decisions

Chlorine test strips used to characterise source-water quality at candidate healthcare facility intakes carry lot-to-lot variability that propagates into any bacteriological forecasting model trained on those readings, yet siting decisions are routinely evaluated as if the predictor were measured without error. The framework treats the chlorine reading as a mismeasured proxy for the latent disinfectant residual, derives the asymptotic bias of the ordinary least squares estimator of the exposure coefficient under classical additive measurement error, and shows how lot-specific calibration offsets inflate the variance of siting-relevant predictions. A replication-based evaluation was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the behaviour of the ordinary least squares estimator was characterised. Across the 600 replications the estimated bias for the intercept was 0.05184 and for the exposure coefficient -0.00094; the root mean squared error was 0.88892 for the intercept and 0.01599 for the exposure coefficient; empirical coverage of the nominal 95% confidence intervals was 1.0 for both parameters. The results indicate that under the declared process the estimator is essentially unbiased for the exposure coefficient and that nominal coverage is attained, which bounds how much of the siting error can be attributed to estimator bias rather than to unmodelled lot effects. The contribution is a formal account of when chlorine-strip lot bias matters for facility siting and when it does not.