Personal Pandemic Proximity Index System for Real-Time Risk Assessment
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Solution Overview
Problem
Current methods lack real-time, patient-specific personal pandemic proximity indices necessary for effective management of infectious disease spread, particularly in identifying and mitigating hotspots and risk exposure for frontline healthcare workers and vulnerable populations.
Innovation Solution
A system and method that generates a real-time personal pandemic proximity index (PPPI) using geospatial modeling, machine learning, and artificial intelligence, integrating data from various sources such as census data, mobility information, and confirmed infection cases to estimate exposure risk and provide actionable insights through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time personal pandemic proximity index system is implemented, then risk assessment accuracy and response effectiveness are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the pandemic risk assessment into multiple independent modules: data collection module, geospatial analysis module, machine learning risk calculation module, and visualization module. Each module handles specific tasks independently, improving measurement precision while managing system complexity through functional decomposition.
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates data from multiple sources (census data, mobility information, infection cases) and transforms it into standardized risk indices. This intermediary layer simplifies the complex interactions between various data sources and the final risk assessment output.
2Measurement precision
If comprehensive data integration from multiple sources is performed, then exposure risk estimation accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing census data, mobility patterns, and infection case data in standardized formats before real-time risk assessment is needed. This allows the real-time system to quickly retrieve and integrate pre-processed data, improving exposure risk estimation accuracy without excessive processing time during critical moments.
Solution Approach 2:
The patent implements continuous data streaming and processing from multiple sources, maintaining an always-updated risk assessment model. This continuous action ensures that the system continuously integrates new infection cases, mobility data, and demographic information without requiring periodic batch processing, thus improving accuracy while managing time efficiency.
3Productivity
If real-time monitoring and identification of hotspots is achieved, then disease spread containment effectiveness is improved, but computational load and processing power requirements increase
Solution Approach 1:
The system applies local quality by focusing computational resources on identifying and analyzing specific geographic hotspots rather than uniformly processing all areas. The machine learning model dynamically adjusts its analysis intensity based on detected risk patterns, concentrating computational power on high-risk zones where disease spread containment is most critical, thus improving effectiveness while managing computational load.
4Measurement precision
If personalized risk indices for individuals are generated, then targeted intervention precision is improved, but data privacy concerns and security requirements increase
Solution Approach 1:
The patent extracts and separates personally identifiable information from the risk assessment process. The system generates personalized risk indices based on aggregated anonymized data patterns rather than processing individual detailed personal data. This extraction approach maintains targeted intervention precision by preserving location and mobility pattern information while removing sensitive personal identifiers that create privacy risks.
Data Source
AI summary
The personal pandemic proximity index system and method include a data ingestion pipeline configured to receive location data associated with disease-positive cases, a data processing module configured to clean and process the received data, a personal pandemic proximity module configured to determine a numerical personal pandemic proximity index value associated with an individual having an interaction at an address relative to the location data, and a graphical user interface configured to present, to a patient care team, the numerical personal pandemic proximity index value, which may include a modified workflow to limit the spread of disease.