Infectious Transmission Detection Using Phenotypic Neighborhood Density
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Solution Overview
Problem
Current methods for detecting infectious transmission in populations are either slow and expensive (genetic sequencing) or less reliable (phenotypic identity implies transmission), making them unsuitable for rapid and large-scale epidemic surveillance.
Innovation Solution
A method using a neighborhood density metric to estimate the probability of direct infectious transmission based on phenotypic data, such as resistance profiles and protein peaks, without the need for genetic sequencing, by calculating the number of similar isolates within a reference distance and determining transmission probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If genetic sequencing method is used to detect infectious transmission, then measurement precision and reliability are improved, but productivity and cost are worsened
Solution Approach 1:
The patent extracts only the essential phenotypic characteristics (antimicrobial susceptibility profiles, protein peaks from MALDI-TOF mass spectrometry) needed for transmission detection, eliminating the need for complete genetic sequencing. This extraction approach maintains sufficient detection accuracy while dramatically reducing time and cost requirements.
Solution Approach 2:
The patent creates a phenotypic fingerprint copy of the pathogen isolate that serves as a surrogate for genetic sequencing. By using phenotypic data (antimicrobial resistance patterns and protein spectral profiles) as a copy representation, the system achieves comparable transmission detection capability without the resource-intensive genetic sequencing process.
2Productivity
If phenotypic identity approach is used to detect infectious transmission, then productivity and cost are improved, but measurement precision and reliability are worsened
Solution Approach 1:
The patent transforms the binary identity comparison approach into a quantitative parameter-based analysis. Instead of simply checking if isolates are identical, the system calculates a neighborhood density metric based on multiple phenotypic parameters (antimicrobial susceptibility, protein peak intensities and positions), enabling graded assessment of relatedness that improves detection accuracy.
Solution Approach 2:
The patent introduces an intermediary computational model (neighborhood density metric) that mediates between phenotypic data and transmission probability assessment. This intermediary layer processes phenotypic similarities through a probabilistic framework, allowing the system to infer transmission likelihood more accurately than direct identity comparison while maintaining rapid phenotypic-based processing.
3Device complexity
If phenotypic identity approach is used to detect infectious transmission, then device complexity is reduced, but reliability is worsened due to false positives and negatives
Solution Approach 1:
The patent combines multiple phenotypic data types (antimicrobial susceptibility profiles and MALDI-TOF mass spectrometry protein spectra) into a composite phenotypic fingerprint. This composite approach creates a more robust and reliable transmission detection system by leveraging the complementary information from different phenotypic measurements, reducing false positives and negatives while maintaining relative simplicity.
Data Source
AI summary
A method for detecting an infectious transmission in a population is disclosed. For a plurality of infectious agent isolates, each associated with an individual of the population, a vector can be obtained with values descriptive of the isolate and distance between two isolates determined. An infectious transmission in the population can be detected as a function of the estimated probabilities of direct infectious transition between each pair of individuals.