Dynamic Risk Assessment for Infection Control
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Solution Overview
Problem
Current contact tracing systems operate at a coarse level of granularity, often requiring GPS data and unable to provide precise risk assessment within enclosed spaces like offices or factories, leading to unnecessary closures and economic losses.
Innovation Solution
A system comprising a processor with a data capturer, process engine, and rules engine that captures and analyzes risk factors associated with infection risks in specific environments, assigning weighted risk scores and profiles to spaces and individuals, enabling fine-grained risk assessment and automated mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If GPS-based contact tracing systems are used, then coverage area is improved, but measurement precision deteriorates due to inability to provide fine-grained risk assessment in enclosed spaces
Solution Approach 1:
The system segments the risk assessment into two distinct layers: a coarse-grained GPS-based layer for outdoor/wide-area contact tracing, and a fine-grained indoor layer using Wi-Fi/BLE sensors for enclosed spaces. This segmentation allows each layer to operate at its optimal precision level without compromising the other, resolving the contradiction between coverage area and measurement precision.
Solution Approach 2:
The system transitions from relying solely on GPS coordinates (2D spatial data) to incorporating multi-dimensional data including Wi-Fi signal strength, BLE device identifiers, time stamps, and location context. This dimensional enrichment enables precise risk assessment in enclosed spaces where GPS is unavailable, simultaneously maintaining wide coverage through the hybrid approach.
2Reliability
If entire facilities are closed down when infection risk is detected, then infection control is improved, but economic loss increases due to unnecessary closures
Solution Approach 1:
The system applies local quality by implementing risk-based zone classification within facilities, where only specific high-risk zones (e.g., meeting rooms, corridors) are identified and targeted for mitigation measures. This allows the facility to remain operational while containing risk to specific areas, thereby maintaining infection control effectiveness without causing unnecessary economic loss from facility-wide closures.
Solution Approach 2:
Instead of applying excessive action (complete facility shutdown), the system implements partial action by targeting only the specific zones and time periods where infection risk exceeds thresholds. This partial mitigation approach maintains sufficient infection control while preserving economic productivity in low-risk areas.
3Device complexity
If coarse-grained contact tracing is implemented, then system complexity is reduced, but loss of information increases due to inability to provide fine-grained risk data
Solution Approach 1:
The system implements dynamic risk scoring that adjusts weights of different risk factors (duration of exposure, proximity, ventilation conditions, asymptomatic status) based on the specific context. This dynamic approach enables the system to capture fine-grained risk information without requiring overly complex static models, as the adaptive weighting simplifies the processing while maintaining information richness.
Solution Approach 2:
The system utilizes existing mobile device sensors and infrastructure (GPS, Wi-Fi, BLE) that are already present in users' devices, rather than requiring dedicated complex tracking hardware. This self-service approach leverages readily available components to capture detailed risk information, reducing system complexity while preventing loss of information.
Data Source
AI summary
A system and method for dynamic risk assessment is disclosed. The system may include a data warehouse, an output device and a processor including a data capturer, a process engine and a rules engine. The data capturer may capture information pertaining to a plurality of risk factors associated with an infection risk corresponding to a plurality of data elements in an environment. The plurality of data elements may pertain to at least one of a space and a person in the space. The process engine may include at least one of a space risk profiler and a person risk profiler. The process engine may determine a risk score and a risk profile associated with the person and the space. Based on the risk profile, the processor may perform at least one of an automatic identification of a mitigation to reduce the infection risk and an automated generation of an alert.