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EpiLearn: A Python Library for Machine Learning in Epidemic Modeling

Zewen Liu, Yunxiao Li, Mingyang Wei, Guancheng Wan, Max S. Y. Lau, Wei Jin

TL;DR

EpiLearn addresses the gap between machine-learning methods and epidemic modeling by providing a Python-based, modular library for forecasting and source detection on epidemic data. The approach combines a broad spectrum of model architectures (temporal, spatial, spatial-temporal) with data processing, simulation, and an interactive web app, all organized into task-oriented pipelines. Key contributions include task-class designs, an extensible pipeline, data transformations, and rapid evaluation tools, enabling researchers to benchmark and develop novel epidemic models. The work has practical impact by lowering barriers for data scientists and epidemiologists to analyze, simulate, and visualize outbreak dynamics within a unified framework.

Abstract

EpiLearn is a Python toolkit developed for modeling, simulating, and analyzing epidemic data. Although there exist several packages that also deal with epidemic modeling, they are often restricted to mechanistic models or traditional statistical tools. As machine learning continues to shape the world, the gap between these packages and the latest models has become larger. To bridge the gap and inspire innovative research in epidemic modeling, EpiLearn not only provides support for evaluating epidemic models based on machine learning, but also incorporates comprehensive tools for analyzing epidemic data, such as simulation, visualization, transformations, etc. For the convenience of both epidemiologists and data scientists, we provide a unified framework for training and evaluation of epidemic models on two tasks: Forecasting and Source Detection. To facilitate the development of new models, EpiLearn follows a modular design, making it flexible and easy to use. In addition, an interactive web application is also developed to visualize the real-world or simulated epidemic data. Our package is available at https://github.com/Emory-Melody/EpiLearn.

EpiLearn: A Python Library for Machine Learning in Epidemic Modeling

TL;DR

EpiLearn addresses the gap between machine-learning methods and epidemic modeling by providing a Python-based, modular library for forecasting and source detection on epidemic data. The approach combines a broad spectrum of model architectures (temporal, spatial, spatial-temporal) with data processing, simulation, and an interactive web app, all organized into task-oriented pipelines. Key contributions include task-class designs, an extensible pipeline, data transformations, and rapid evaluation tools, enabling researchers to benchmark and develop novel epidemic models. The work has practical impact by lowering barriers for data scientists and epidemiologists to analyze, simulate, and visualize outbreak dynamics within a unified framework.

Abstract

EpiLearn is a Python toolkit developed for modeling, simulating, and analyzing epidemic data. Although there exist several packages that also deal with epidemic modeling, they are often restricted to mechanistic models or traditional statistical tools. As machine learning continues to shape the world, the gap between these packages and the latest models has become larger. To bridge the gap and inspire innovative research in epidemic modeling, EpiLearn not only provides support for evaluating epidemic models based on machine learning, but also incorporates comprehensive tools for analyzing epidemic data, such as simulation, visualization, transformations, etc. For the convenience of both epidemiologists and data scientists, we provide a unified framework for training and evaluation of epidemic models on two tasks: Forecasting and Source Detection. To facilitate the development of new models, EpiLearn follows a modular design, making it flexible and easy to use. In addition, an interactive web application is also developed to visualize the real-world or simulated epidemic data. Our package is available at https://github.com/Emory-Melody/EpiLearn.
Paper Structure (12 sections, 2 figures)

This paper contains 12 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Overview of EpiLearn
  4. Tasks in Epidemic Modeling
  5. Epidemic Models
  6. Data Processing
  7. Data Simulation
  8. Epidemic Modeling Pipeline
  9. Code Demonstration
  10. Interactive Application
  11. Key Designs and Project Focus
  12. Impacts and Future Plans

Figures (2)

  • Figure 1: Overview of EpiLearn.
  • Figure 2: The interface developed for visualizing epidemic data.