Interpretable machine-learning prediction of household risk of trachoma and spatial hotspot mapping in Ethiopia

Abstract Background Active trachoma remains above elimination thresholds in Ethiopia, yet tools for fine-scale targeting are limited. We developed and internally validated an interpretable machine-learning model to predict household-level risk of trachomatous inflammation–follicular (TF) and mapped hotspots to guide SAFE implementation. Methods A community-based cross-sectional survey was conducted in Dessie Zuria District (March–May 2024) among households with children aged 1–9 y. TF was graded using the amended WHO simplified system. Behavioral, environmental and sociodemographic predictors were collected using WHO-aligned tools. A survey-weighted ridge-penalized logistic regression with nested cross-validation was trained, with missing data imputed within folds. Model performance (AUC, calibration, Brier score) and decision-curve analysis were evaluated using kebele-cluster bootstrapping. Spatial clustering was assessed using Moran’s I and false discovery rateadjusted Getis–Ord Gi*. Results Among 612 households (928 children), 9.8% of households and 9.1% of children were TF-positive. Key predictors included unclean child face, absence of latrine, water-collection time >30 minutes, flies on the face and low hygiene score. The model showed strong discrimination (AUC = 0.84), good calibration (slope 0.98) and accuracy (Brier 0.067). Conclusions The model identifies high-risk households and spatial hotspots, supporting targeted F/E interventions to accelerate trachoma elimination.