Predictive Contact Tracing via Probabilistic Infection Modeling
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Solution Overview
Problem
Manual contact tracing during pandemics is prone to errors due to reliance on human memory and is inefficient, leading to inaccurate identification of infected individuals and potential clusters, which can exacerbate the spread of infectious diseases.
Innovation Solution
A computer-implemented method using timestamped location data and medical information to create a probabilistic model, specifically a Markov network, that predicts the probability of infection among individuals who have been in contact with an infected person, enabling automated and accurate contact tracing and future infection prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual contact tracing is used, then human memory and judgment are utilized to identify contacts, but accuracy deteriorates due to errors in human memory and inefficiency in processing
Solution Approach 1:
The patent replaces the manual mechanical process of contact tracing with an automated computer-based system. The system uses algorithms to process location data, create contact graphs, and identify potential exposures automatically, eliminating human memory errors and significantly improving both accuracy and efficiency of contact identification.
Solution Approach 2:
The system enables self-service contact tracing by automatically collecting location data, building contact graphs, and identifying exposures without requiring manual intervention. The automated processing of timestamped location data and medical information allows the system to serve itself in performing contact tracing functions.
2Ease of manufacture
If manual contact tracing is used, then simplicity of implementation is maintained, but reliability deteriorates due to errors in identification leading to spread of infection
Solution Approach 1:
The patent replaces unreliable manual processes with a reliable automated computer-based system that processes location data and medical information systematically. This substitution maintains ease of implementation through automated workflows while dramatically improving reliability by eliminating human error in contact identification and exposure assessment.
3Measurement precision
If probabilistic modeling is implemented, then prediction accuracy is improved, but device complexity increases due to sophisticated algorithms and data processing requirements
Solution Approach 1:
The patent segments the complex probabilistic modeling task into distinct modular components: retrieving timestamped location data, creating contact graphs, retrieving medical data, and running the probabilistic model. This segmentation manages system complexity by organizing the sophisticated algorithm into manageable, independent modules that can be developed and maintained separately.
Solution Approach 2:
The patent introduces contact graphs as an intermediary data structure that bridges raw location data and the probabilistic model. The contact graph serves as a mediator that organizes spatial and temporal information in a structured format, simplifying the input requirements for the probabilistic modeling algorithm and making the system more manageable.
Data Source
AI summary
In an approach to predictive contact tracing, a computer receives a query associated with contact tracing of a person with an infection. A computer retrieves timestamped location data associated with the person over a period of time. Based on the retrieved data, a computer creates a contact graph associated with the person, where the contact graph depicts one or more other people that were in contact with the person over the period of time. A computer retrieves medical data associated with the person and the one or more other people that were in contact with the person over the period of time. Based on the retrieved data, a computer builds a probabilistic model. A computer runs the probabilistic model to provide a prediction of a probability of infection of the one or more other people over the period of time as a result of being in contact with the person.