Evolutionary Surrogate Models for Epidemiological NPI Optimization
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Solution Overview
Problem
Existing epidemiological models struggle to accurately predict and prescribe effective non-pharmaceutical interventions (NPIs) due to uncertainty in data and nonlinear interactions, requiring costly computational resources and lacking automated decision-making capabilities.
Innovation Solution
An Evolutionary Surrogate-assisted Prescription (ESP) approach using a predictor model to forecast outcomes and a prescriptor model to optimize NPI strategies, combining evolutionary search with surrogate modeling to discover effective NPIs from limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional epidemiological models are used to predict disease spread, then model simplicity is maintained, but prediction accuracy deteriorates due to uncertainty in parameters and nonlinear interactions
Solution Approach 1:
The patent introduces an evolutionary surrogate model as an intermediary between traditional epidemiological models and complex real-world dynamics. This surrogate model, trained on historical data, captures nonlinear interactions and parameter uncertainties without requiring explicit modeling of all underlying mechanisms, thus improving prediction accuracy while avoiding the computational burden of fully complex models
Solution Approach 2:
The patent dynamically adjusts model parameters through evolutionary optimization based on incoming data and changing pandemic conditions. Instead of using fixed parameters, the system evolves parameter values to adapt to new information, thereby improving prediction accuracy in response to uncertainty and nonlinear interactions
2Measurement precision
If comprehensive data collection and complex modeling are employed to overcome uncertainty, then prediction accuracy improves, but computational cost increases
Solution Approach 1:
The patent employs a lightweight evolutionary surrogate model that can be rapidly trained and updated with minimal computational resources. Rather than using expensive, computationally intensive models, the system uses efficient machine learning algorithms that provide sufficient accuracy at lower computational cost, enabling continuous updating as new data arrives
Solution Approach 2:
The patent collects and utilizes only the most critical features and data elements necessary for accurate prediction, rather than processing all available data comprehensively. This selective approach to data collection and modeling achieves sufficient prediction accuracy while significantly reducing computational requirements
3Reliability
If evolutionary search is used to discover optimal NPI strategies, then solution quality improves, but computational time increases
Solution Approach 1:
The patent performs preliminary training of the evolutionary surrogate model on historical data before deployment. This pre-computation phase captures general patterns and relationships, so that during actual NPI strategy optimization, the evolutionary search operates on a pre-prepared model rather than learning from scratch, significantly reducing real-time computational time while maintaining solution quality
Solution Approach 2:
The patent implements a dynamic evolutionary search process that adapts its parameters and search intensity based on the problem state and available computational resources. The system can adjust the balance between exploration and exploitation, and modify search depth based on time constraints, thereby achieving good solution quality with reduced computational time
Data Source
AI summary
The present invention relates to an ESP decision optimization system for epidemiological modeling. ESP based modeling approach is used to predict how non-pharmaceutical interventions (NPIs) affect a given pandemic, and then automatically discover effective NPI strategies as control measures. The ESP decision optimization system comprises of a data-driven predictor, a supervised machine learning model, trained with historical data on how given actions in given contexts led to specific outcomes. The Predictor is then used as a surrogate in order to evolve prescriptor, i.e. neural networks that implement decision policies (i.e. NPIs) resulting in best possible outcomes. Using the data-driven LSTM model as the Predictor, a Prescriptor is evolved in a multi-objective setting to minimize the pandemic impact.