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austin edited this page Jul 24, 2023 · 35 revisions

BINNs_Covasim_EQL Wiki

This is the wiki for the BINNs_Covasim_EQL repository explaining the motivation, goals, methods, and results of this project as well as information of the computational pipeline for 1) Modifying the agent-based simulator, 2) Training Biologically-Informed Neural Networks on the data, and 3) Conducting Equation Learning on the trained parameter networks.

This wiki is the guideline explaining and connecting our paper, "Incorporating Human Adaptive Behaviors into Epidemiological Models using Equation Learning", and our code repository. For more information on our project, please see [INSERT PAPER LINK].

Table of Contents

This wiki is structured in an order similar to the intended computational pipeline created.

  1. Overview: This page gives the overarching idea behind the project and the table of contents guiding the reader through the content.
  2. Data Generation: Learn about what an agent-based model (ABM) is, our ABM - Covasim, how to interact with Covasim, how to generate data, how to add an adaptive behavior, and why we chose masking as our adaptive behavior.
  3. Biologically-Informed Neural Networks (BINNs): Learn about what BINNs are and the role they play in the equation learning process. Learn about our model's architecture, how it works, the different types of models available for our application, and why we designed it in this way.
  4. Learned Parameter Equation Regression: This is the equation learning process done to find closed form equations that approximate the ODE system. Learn how to perform sparse regression on equations that infer the learned parameter networks, how our algorithm works, and how to interpret results.
  5. Learned Parameter Surface Plotting: Learn how to plot the surfaces learned by the BINNs.
  6. Evaluation Procedure: This portion of the process numerically solves the system of ODEs using the learned parameter networks and learned equations.
  7. Sensitivity Analysis: Conduct sensitivity analysis to determine the parameters in the learned equations.

That's it! Pages 2-7 take you through the entire computational pipeline we created. In reality, only pages 2-4 and 6 are required in order to perform equation learning. However, page 5 provides a visual interpretation for the learned networks and page 7 provides a quantifiable procedure for analyzing the learned equations and what components in those equations are important to describe the learned parameter.

Overview

[Overview of agent-based models, what they lack, what Covasim lacked, what we added, why we added it, why using BINNs, and why do equation learning.]

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