Machine Learning Models Predict Preventative Measure Efficacy
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Solution Overview
Problem
Conventional methods for analyzing the effectiveness of preventative measures against the spread of COVID-19 and other pandemics are qualitative and not tested in real-world scenarios, lacking accuracy.
Innovation Solution
The use of data science and machine learning techniques to analyze data from facilities implementing preventative measures, such as sanitation, distancing, and air filtration, to predict their efficacy in mitigating the spread of pathogens and illnesses, providing quantitative risk assessments and recommendations for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional qualitative methods are used to analyze preventative measures, then the analysis process is simple and quick, but the accuracy and reliability of the analysis is insufficient
Solution Approach 1:
The patent replaces conventional qualitative analysis methods with machine learning models that process quantitative data from facilities. The system uses trained ML models to predict the efficacy of preventative measures based on real-world data, substituting manual qualitative assessment with automated computational analysis to improve accuracy.
Solution Approach 2:
The patent introduces an intermediary data processing layer between preventative measure implementation and efficacy assessment. The system collects data from facilities, processes it through machine learning models, and generates quantitative predictions about effectiveness, serving as a mediator that transforms raw data into actionable insights.
2Adaptability or versatility
If laboratory conditions are used for analysis, then the controlled environment provides reliable data, but the results cannot be applied to real-world scenarios
Solution Approach 1:
The patent performs preliminary training of machine learning models using data from facilities that have already implemented preventative measures in real-world settings. The models are trained on historical data before being deployed to make predictions, allowing them to learn from actual operational conditions rather than controlled laboratory environments.
Solution Approach 2:
The patent creates a digital copy of real-world facility data and uses it to train machine learning models. By replicating and processing actual operational data from facilities implementing preventative measures, the system captures real-world complexity while maintaining analytical reliability through structured data processing.
3Measurement precision
If quantitative data collection from facilities is implemented, then the accuracy of efficacy prediction is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent designs a universal data collection framework that can gather information from multiple facility types and preventative measure implementations through a single integrated system. The machine learning models are trained to handle diverse data formats and facility configurations, allowing the same system to analyze different scenarios without requiring separate specialized collection methods for each.
Data Source
AI summary
Embodiments of the present disclosure generally relate to methods for analyzing the effectiveness of preventative measures on the spread of illnesses, such as COVID-19, on living organisms. More particularly, embodiments of the present disclosure relate to methods for identifying the effectiveness of preventative measures, processes, equipment and other available data, and providing indicators and methods of visualization the effectiveness of preventative measures on the spread of an illness.