Temporal Graph Infection Risk Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current health surveillance systems for infectious diseases face challenges such as slow data collection, time lags in disease progression tracking, low temporal and spatial resolution, and limited disease spreading modalities, which hinder rapid response and effective infection risk prediction.
Innovation Solution
The system generates temporal graphs from disease progression data, combines node embeddings from personal and geographic networks using attributed random walks and machine learning algorithms to predict infection risks, identify susceptible interactions, and create dynamic geo-fencing zones, enabling rapid response and intervention planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional health surveillance systems are used, then disease tracking is performed, but the data collection is slow and temporal resolution is low
Solution Approach 1:
The patent replaces traditional mechanical health surveillance systems with a computational graph-based system that processes disease progression data through temporal graph embeddings and machine learning models, enabling rapid prediction of infection risks without the time lags inherent in conventional reporting mechanisms
Solution Approach 2:
The system performs preliminary actions by continuously generating temporal graphs and updating node embeddings in real-time as new disease data arrives, maintaining up-to-date infection risk predictions before outbreaks occur rather than reacting after data collection delays
2Measurement precision
If traditional surveillance systems are used, then disease tracking is performed, but spatial resolution is low
Solution Approach 1:
The patent applies local quality by creating location-specific node embeddings for geographic regions that capture local disease transmission characteristics, allowing precise spatial resolution of infection risks while preserving information about local spreading modalities through the heterogeneous graph structure
Solution Approach 2:
The system adds spatial dimensionality by incorporating geographic location as a explicit node attribute and creating spatial-temporal graphs that track disease progression across multiple geographic levels (neighborhood, city, region), transforming limited spatial data into high-resolution spatial predictions
3Reliability
If simple surveillance models are used, then implementation is straightforward, but infection risk prediction accuracy is limited
Solution Approach 1:
The patent segments the complex prediction task into distinct computational components: temporal graph construction, node embedding generation, embedding combination, and risk prediction modeling. This segmentation allows the system to achieve high prediction accuracy through specialized processing at each stage while managing overall system complexity through modular architecture
Data Source
AI summary
Predicting infection risk by generating a first temporal graph of a first set of disease progression data, generating a second temporal graph of a second set of disease progression data, combining a first temporal graph node embedding and a second temporal graph node embedding, and generating a predicted infection risk according to the first temporal graph node embedding and the second temporal graph node embedding.