Additive Compendium Map of Outbreak Risk Determinants of West Nile Virus in Europe at NUTS3

Annual emergence of West Nile virus depends on a complex transmission chain. Predictive efforts are consequently confounded by time-varying associations and scale-dependent effect variability. SHAP (SHaply Additive Explanation) is a novel AI-driven solution with potential to overcome this. SHAP takes a high-performance XGBoost model and deductively imputes the marginal contribution of each feature with respect to the log relative risk associated with the local XGBoost prediction (an additive model). The resulting effect matrix is dimensionally identical to the original data but IID and homogenized in terms of units, scale, and interpretation. Such "synthetic data" can therefore serve as surrogate to allow for high-power statistical analyses. Here, we applied SHAP to a database consisting of high-resolution data from various domains - climate, environment, economic, sociodemographic, vector and host distribution - to derive an effect matrix of WNV outbreak risk determinants in Europe. This effect data proved superior to the original, nominal data in predictive tasks and delivered qualitatively compelling, domain-specific risk mappings. Further applications are discussed and others are invited to experiment.