Coccidioidomycosis (Valley fever, VF) is a climate‐sensitive infectious disease caused by inhaling soil‐dwelling fungus Coccidioides , mostly reported in southwestern USA. Although soil moisture (SM) and soil temperature (ST) are known to shape the fungal lifecycle, their effects on coccidioidomycosis remain understudied. Most prior studies have relied on their proxies—precipitation and air temperature—that might not accurately capture soil hydrothermal dynamics. We conducted multivariable negative binomial regressions to estimate seasonal associations between incidence and climate drivers—including SM, ST, and wind speed from the North American Land Data Assimilation Phase 2 (NLDAS‐2), and PM ₁₀ ‐based dusty‐day counts—in Arizona's hyperendemic counties (Maricopa, Pima, and Pinal) from 2000 to 2022. We found higher incidence in areas with hotter, drier soils and more seasonal dusty days. Multi‐year soil hydrothermal cycles—alternating wet–dry and cool–hot periods along with concurrent dry, dusty conditions—significantly influenced incidence. Notably, no antecedent dry–cool seasons were linked to increased incidence, indicating moisture and/or heat are prerequisites for fungal growth and dispersal. SM showed more consistent and widespread effects than ST across seasons and lags, with winter and spring soils most influential. Higher incidence followed wetter winters and monsoons, and dry, hot springs and falls. Our models using NLDAS‐2 SM and ST data showed robust performance and generalizability across exposure seasons. Our results support adding multi‐year soil indicators—with up to 3‐year lead times—into early‐warning systems to enhance VF forecasting and better prepare endemic regions for the challenges of a warming, drying, and increasingly variable climate.

Coccidioidomycosis (Valley fever) is a soilborne mycosis endemic to the US Southwest whose incidence has increased markedly in recent decades. Environmental conditions are thought to influence the soil-dwelling lifecycle of Coccidioides; however, most prior studies have relied on above-ground meteorological conditions - primarily precipitation and air temperature (AT) - with few examining subsurface soil moisture (SM) and soil temperature (ST), which may more directly influence the fungal lifecycle. No study of coccidioidomycosis, or any other environment-sensitive soilborne mycosis, has examined how deeper-layer soil conditions relate to disease incidence, despite the prevailing soil-sterilisation hypothesis implicating deeper soil as a potential fungal refugium. Furthermore, nonlinear exposure-lag-response relationships for key dust-dispersion exposures - including PM10, a potential proxy for airborne spore concentration, and wind speed - remain uncharacterised. We aimed to estimate and compare the associations between coccidioidomycosis incidence and environmental exposures across multiple above- and below-ground layers, and to evaluate their independent and combined predictive performance. This ecological time-series study analysed 185,486 reported cases of coccidioidomycosis in Arizona's hyperendemic tri-county region (Maricopa, Pima, and Pinal) during 1997-2024. We developed a mechanism-informed multilayer environmental framework comprising one dust-dispersion layer (PM10, wind speed) and four soil-climate layers - meteorological (precipitation, AT), topsoil (0-10 cm), midsoil (10-40 cm), and deepsoil (40-100 cm) SM and ST. We fitted distributed lag non-linear models (DLNMs) with season-specific interaction terms to estimate exposure-lag-response associations between each environmental layer and coccidioidomycosis incidence. We then developed a two-stage stacked ensemble machine learning framework to assess each layer's independent predictive performance (stage 1) and integrate them into a unified forecast (stage 2), which was evaluated using a strictly held-out test period. At concurrent lags (1-3 months prior to reporting), coccidioidomycosis incidence was primarily associated with dustier, windier, and drier conditions, cooler air temperatures, and warmer topsoil. For each IQR increase, PM10 showed the most consistent concurrent associations, with significant positive incidence rate ratios (IRRs) across all four incidence seasons at lags 1-2 (ranging from 1.04 [95% CI 1.00-1.08] to 1.37 [1.26-1.48]). Across lags 1-36 months, all four soil-climate layers exhibited nonlinear, non-monotonic, and season-dependent associations with coccidioidomycosis incidence, characterised by alternating wet-dry and cool-warm oscillations. Topsoil displayed the most frequent significant associations, with moisture-temperature signals attenuating progressively from topsoil through midsoil to deepsoil. A depth-dependent lag structure was observed for both moisture and temperature, in which significant positive IRRs emerged at progressively shorter lags with increasing soil depth, accompanied by vertical divergence across depths at the same lag windows. For example, for fall incidence, positive moisture IRRs appeared at precipitation lag 15 (1.10 [1.00-1.21]), topsoil SM lag 9 (1.22 [1.15-1.30]), midsoil SM lags 8-9 (up to 1.23 [1.11-1.37]), and deepsoil SM lags 4-5 (up to 1.08 [1.01-1.16]); at these same lags, deepsoil SM was positively associated with incidence whereas topsoil SM and precipitation remained negatively associated. During the held-out test period (2021-2024), the multilayer ensemble generally captured seasonal and interannual variation well, including the timing and approximate magnitude of most peaks, outperforming all single-layer models. Although individual layers had slightly lower test RMSEs, their test gap ratios were substantially higher (0.27-0.67 vs 0.00), indicating that the ensemble generalised far more reliably. All five environmental layers contributed to the final ensemble forecast; the dust-dispersion and topsoil layers received the highest importance, with PM10 ranked as the most important predictor group. The best-performing of four pipeline configurations relied solely on environmental inputs available within one week, enabling the model to function as a near-real-time nowcast. This study provides the first evidence linking multilayer environmental exposures to coccidioidomycosis incidence across both temporal and vertical dimensions, offering new quantitative support for the prevailing soil-sterilisation and grow-and-blow hypotheses and demonstrating that a multilayer framework could improve both mechanistic understanding and predictive performance. The framework could be generalised to other endemic settings and readily extended with new data and methods to inform surveillance and public health preparedness. These findings support incorporating multilayer lagged environmental exposures into both effect estimation and forecasting systems to better prepare endemic regions for anticipated warming, drying, and increasingly variable climatic conditions.

Coccidioidomycosis (Valley fever, VF) is a climate-sensitive infectious disease caused by inhaling soil-dwelling fungus Coccidioides , mostly reported in southwestern USA. Although soil moisture (SM) and soil temperature (ST) are known to shape the fungal lifecycle, their effects on coccidioidomycosis remain understudied. Most prior studies have relied on their proxies—precipitation and air temperature—that might not accurately capture soil hydrothermal dynamics. We conducted multivariable negative binomial regressions to estimate seasonal associations between incidence and climate drivers—including SM, ST, and wind speed from the North American Land Data Assimilation phase 2 (NLDAS-2), and PM10-based dusty-day counts—in Arizona's hyperendemic counties (Maricopa, Pima, and Pinal) from 2000 to 2022. We found higher incidence in areas with hotter, drier soils and more seasonal dusty days. Multi-year soil hydrothermal cycles—alternating wet–dry and cool–hot periods along with concurrent dry, dusty conditions—significantly influenced incidence. Notably, no antecedent dry–cool seasons were linked to increased incidence, indicating moisture or heat as prerequisites for fungal growth or dispersal. SM showed more consistent and widespread effects than ST across seasons and lags, with winter and spring soils most influential. Higher incidence followed wetter winters and monsoons, and dry, hot springs and falls. Our models using NLDAS-2 SM and ST data showed robust performance and generalizability across exposure seasons. Our results support adding multi-year soil indicators—with up to three-year lead times—into early-warning systems to enhance VF forecasting and better prepare endemic regions for the challenges of a warming, drying, and increasingly variable climate.