mlsanityData to 7 Oct 2026

EpiLearn: A Python Library for Machine Learning in Epidemic Modeling

arXiv 2406.06016 ↗First posted 10 Jun 2024cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

15 of 53 references.

References · 15

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3 more
  1. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  2. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
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Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preferencecited by 16
  2. 2025Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networkscited by 14
  3. 2024Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graphcited by 10
  4. 2025U-Cast: Learning Hierarchical Structures for High-Dimensional Time Series Forecastingcited by 5
  5. 2025Pre-training Epidemic Time Series Forecasters with Compartmental Prototypescited by 3