Machine learning ensemble methods for prospective hantavirus risk prediction: integrating environmental and epidemiological data

We develop an ensemble machine learning framework integrating environmental, ecological, and socioeconomic variables to enable prospective hantavirus risk prediction. Trained on 689 laboratory-confirmed cases from three United States jurisdictions, the ensemble of random forest, gradient boosting, support vector machine, and logistic regression classifiers achieves area under the receiver operating characteristic curve of 0.92 on independent test data. Shapley additive explanations identify precipitation variability, land-use patterns, and rodent species richness as dominant predictors, with substantial contributions from socioeconomic determinants.