Forecasting Coccidioidomycosis (Valley Fever) in Arizona: A Graph Neural Network Approach
This addresses a public health problem in endemic regions by potentially improving early warning systems and resource allocation for disease prevention.
This study tackled forecasting Valley Fever incidence in Arizona by developing the first graph neural network model that integrates case data with environmental predictors, resulting in effective modeling of disease trends and insights into key environmental drivers.
Coccidioidomycosis, commonly known as Valley Fever, remains a significant public health concern in endemic regions of the southwestern United States. This study develops the first graph neural network (GNN) model for forecasting Valley Fever incidence in Arizona. The model integrates surveillance case data with environmental predictors using graph structures, including soil conditions, atmospheric variables, agricultural indicators, and air quality metrics. Our approach explores correlation-based relationships among variables influencing disease transmission. The model captures critical delays in disease progression through lagged effects, enhancing its capacity to reflect complex temporal dependencies in disease ecology. Results demonstrate that the GNN architecture effectively models Valley Fever trends and provides insights into key environmental drivers of disease incidence. These findings can inform early warning systems and guide resource allocation for disease prevention efforts in high-risk areas.