Smart Pooling System for Infectious Disease Testing
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Solution Overview
Problem
Current laboratory testing methods for infectious diseases face challenges in reducing turnaround times, making it difficult to quickly identify and isolate positive cases, thereby hindering the prevention of infectious disease spread.
Innovation Solution
A system and method for smart pooling that utilizes a computing device to obtain feature data, identify predictive prevalence values through machine-learning models, and determine enhanced well counts, thereby optimizing sample processing and analysis time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional laboratory testing methods are used, then testing accuracy is maintained, but turnaround time is prolonged (1-5 days)
Solution Approach 1:
The patent divides the testing process into pooling and individual testing segments. Samples are grouped into pools for initial bulk testing, and only pools testing positive undergo individual sample retesting. This segmentation reduces the total number of tests needed and accelerates turnaround time while maintaining accuracy.
Solution Approach 2:
The system performs preliminary pooling and bulk testing before individual sample analysis. By pre-grouping samples and conducting initial screening on pools, the system eliminates the need to test every individual sample sequentially, thereby reducing overall testing time and increasing throughput.
2Loss of time
If sample pooling is increased to reduce testing time, then turnaround time improves, but measurement precision deteriorates
Solution Approach 1:
The testing process is segmented into two stages: pool-level screening and individual-sample confirmation. This segmentation allows large pooling (which saves time) to be combined with individual testing (which ensures precision), thereby resolving the contradiction between processing speed and detection accuracy.
Solution Approach 2:
The pool acts as an intermediary between individual samples and the testing system. By first testing pools as intermediaries, the system can quickly screen large numbers of samples while maintaining the ability to identify and individually test positive cases, thus preserving measurement precision.
3Ease of operation
If fixed pooling strategies are used, then operational simplicity is maintained, but adaptability to varying prevalence rates deteriorates
Solution Approach 1:
The patent implements dynamic pooling strategies where pool size and composition are adjusted based on real-time prevalence data and epidemiological conditions. This dynamic approach allows the system to automatically adapt to changing prevalence rates while maintaining ease of operation through automated calculation and assignment.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor testing results and prevalence rates, then use this information to adjust pooling strategies. This feedback loop enables automatic adaptation to varying prevalence conditions while keeping operational complexity manageable through algorithmic decision-making.
Data Source
AI summary
A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.