  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.
