Valley-Forecast: Forecasting Coccidioidomycosis incidence via enhanced LSTM models trained on comprehensive...

Coccidioidomycosis (cocci), or more commonly known as Valley Fever, is a fungal infection caused by Coccidioides species that poses a significant public health challenge, particularly in the semi-arid regions of the Americas, with notable prevalence in California and Arizona. Previous epidemiological studies have established a correlation between cocci incidence and regional weather patterns, indicating that climatic factors influence the fungus's life cycle and subsequent disease transmission. This study hypothesizes that Long Short-Term Memory (LSTM) and extended Long Short-Term Memory (xLSTM) models, known for their ability to capture long-term dependencies in time-series data, can outperform traditional statistical methods in predicting cocci outbreak cases. Our research analyzed daily meteorological features from 2001 to 2022 across 48 counties in California, covering diverse microclimates and cocci incidence. The study evaluated 846 LSTM models and 176 xLSTM models with various fine-tuning metrics. To ensure the reliability of our results, these advanced neural network architectures are cross analyzed with Baseline Regression and Multi-Layer Perceptron (MLP) models, providing a comprehensive comparative framework. We found that LSTM-type architectures outperform traditional methods, with xLSTM achieving the lowest test RMSE of 282.98 (95% CI: 259.2-306.8) compared to the baseline's 468.51 (95% CI: 458.2-478.8), demonstrating a reduction of 39.60% in prediction error. While both LSTM (283.50, 95% CI: 259.7-307.3) and MLP (293.14, 95% CI: 268.3-318.0) also showed substantial improvements over the baseline, the overlapping confidence intervals suggest similar predictive capabilities among the advanced models. This improvement in predictive capability suggests a strong correlation between temporal microclimatic variations and regional cocci incidences. The increased predictive power of these models has significant public health implications, potentially informing strategies for cocci outbreak prevention and control. Moreover, this study represents the first application of the novel xLSTM architecture in epidemiological research and pioneers the evaluation of modern machine learning methods' accuracy in predicting cocci outbreaks. These findings contribute to the ongoing efforts to address cocci, offering a new approach to understanding and potentially mitigating the impact of the disease in affected regions.