Multipathogen respiratory infections have emerged as a critical public health concern. Common respiratory pathogens—including influenza viruses, SARS-CoV-2 (COVID-19),Mycoplasma pneumoniae, respiratory syncytial virus (RSV), and rhinoviruses—circulate widely, particularly during autumn, winter, and seasonal transitions. Globally, the epidemiology of respiratory diseases is complex and dynamic, with multiple pathogens co-circulating or alternating in dominance, posing significant challenges to public health systems.
To improve the prediction and early warning of these pathogens, Dr. Chen Tianmu’s team has developed aRespiratory Multipathogen Prediction and Early-warning Platformusing open-source data. The platform is designed to forecast transmission trends and issue timely alerts for emerging outbreaks.
Platform Features:
1. Multi-pathogen and Single-pathogen Prediction
The platform employs multiple neural network models—including Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Temporal Convolutional Networks (TCN), and Extreme Gradient Boosting (XGBoost)—trained on open-source data. These models generate three-week forecasts for 11 common respiratory pathogens.
2. Prediction and Early Warning for 11 Individual Pathogens
Using the Moving Epidemic Method (MEM), the platform calculates dynamic warning thresholds for each pathogen based on historical data. When forecasted case numbers exceed these thresholds, the system triggers an automated early warning.
3. AI-Generated Analytical Reports
Integrated with DeepSeek-R1, the platform rapidly synthesizes historical trends, forecasted outcomes, and alert statuses to generate concise analytical reports. These reports include key insights and actionable recommendations for public health response.
Platform Availability & Disclaimer
The platform is currently in its beta testing, and we welcome feedback from experts to refine its performance.
To ensure responsible use,access is restricted to qualified professionals (free registration required).
Register Here: https://multiple.ctmodelling.cn/register
Platform Access: https://multiple.ctmodelling.cn/
Important Note: Forecast accuracy may vary due to model limitations and data availability.Interpret results with caution.
