Authors: Harsha Sammang, Harshini Balaga, Aditya Jagatha
Abstract: Coccidioidomycosis (Valley fever), caused by Coccidioides spp., is a climate- and soil-mediated respiratory disease whose exposure arises from inhalation of spores entrained by wind from disturbed, desiccated soils. Incidence is rising across the U.S. Southwest and expanding arid zones. Traditional surveillance is retrospective and weakly coupled to dust-generating processes (drought, grading, off-road activity), limiting actionable lead time for clinicians, public health, and occupational safety. We present Aeolus-DS, a Design Science Research (DSR) artifact that fuses aerosol remote sensing (MAIAC AOD; dust fraction), mesoscale meteorology and soil moisture (ERA5), land-disturbance telemetry (construction and energy activity; off-highway vehicle events; nightlights), and clinical sentinel signals (syndromic ED chief complaints; pneumonia rule-out) into a dust-aware, AI-driven early warning and deci- sion support system. Methodologically, we propose a graph spatiotemporal transformer with direction-aware attention and physics-guided regularization reflecting aeolian transport. Us- ing county–week panels (2014–2024) for AZ–CA–NV, Aeolus-DS improves nowcasting MAE by 18% and two-week AUPRC by 21% over strong baselines (XGBoost, LSTM). Role-based “action cards” translate probabilistic forecasts and uncertainty into targeted mitigations (site watering cadence, temporary grading pauses, N95 staging, clinician test prompts). We eval- uate predictive skill, calibration, runtime, interpretability, and stakeholder usability, and discuss governance, ethics, and portability to other dust-borne mycoses in climate-stressed regions.
DOI: https://doi.org/10.5281/zenodo.17063019
