Outlet Title
IJSr
Document Type
Article
Publication Date
Spring 7-2025
Abstract
This paper presents Aeolus-DS, a design science research (DSR) artifact that integrates aerosol remote sensing (MAIAC AOD; dust fraction), mesoscale meteorology and soil moisture (ERA5), land-disturbance telemetry (construction, off-road vehicle activity, nightlights) and clinical sentinel signals (syndromic ED chief complaints, pneumonia rule-out) into a dust-aware, AI-driven early warning and decision-support system for Coccidioidomycosis (Valley Fever). Methodologically, we propose a graph spatiotemporal transformer with direction-aware attention and physics-guided regularisation reflecting aeolian transport. Using county-week panels (2014-2024) for the U.S. Southwest (AZ–CA–NV), Aeolus-DS improves now-casting mean absolute error (MAE) by 18% and two-week area under precision-recall curve (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 evaluate predictive skill, calibration, runtime, interpretability and stakeholder usability, and discuss governance, ethics and portability to other dust-borne mycoses in climate-stressed regions.
Recommended Citation
This paper is archived in Beadle Scholar for institutional access and indexing. Originally published in IJSR
Comments
This article is deposited here for DSU institutional archiving and indexing. Originally published in IJSR