Reckell, T.; Sterner, B.; Engelthaler, D.; Jevtic, P.

Coccidioidomycosis (Valley fever) is an environmentally acquired fungal infection endemic to arid regions of the Americas and represents a growing public health concern as climate variability alters exposure risk across both established and emerging endemic zones. Existing forecasting approaches have largely relied on statistical associations with meteorological variables, which often fail to capture the nonlinear interactions between the saprobic (environmental) and parasitic (host) life cycles of Coccidioides, particularly under non-stationary environmental conditions. Here, we develop a hierarchy of mechanistic ordinary differential equation (ODE) models that explicitly link environmental drivers to distinct stages of the fungal life cycle. The modeling framework is constructed incrementally, incorporating temperature-dependent growth, soil moisture dynamics, and wildlife reservoir processes, and is calibrated against human case data from multiple regions in Arizona. We derive a climate-dependent environmental reproduction number that characterizes fungal persistence as a function of temperature and soil moisture. Model performance is evaluated using relative root mean square error, information criteria, and out-of-sample forecasting assessed via Diebold-Mariano and modified Diebold-Mariano tests. Models that incorporate only continuous fungal growth perform worse than statistical baselines, while the inclusion of environmental forcing substantially improves predictive accuracy. Further incorporation of wildlife reservoir dynamics yields statistically significant improvements in forecasting performance over phenomenological models. Together, these results demonstrate that integrating environmental variability and ecological amplification mechanisms into mechanistic models enhances the ability to reproduce and forecast Valley fever dynamics. This framework provides a biologically grounded approach for understanding environmentally driven transmission and may inform public health responses under changing climate conditions.