epilearn 0.0.14

A Pytorch library for machine learning in epidemic modeling

Epidemic Modeling with Python

Documentation | Paper | Web Interface

Colab Tutorial

EpiLearn is a Python machine learning toolkit for epidemic data modeling and analysis. We provide numerous features including:

• Implementation of Epidemic Models
• Simulation of Epidemic Spreading
• Visualization of Epidemic Data
• Unified Pipeline for Epidemic Tasks

For more machine models in epidemic modeling, feel free to check out our curated paper list Awesome-Epidemic-Modeling-Papers.

Installation

From Source

git clone https://github.com/Emory-Melody/EpiLearn.git
cd EpiLearn

conda create -n epilearn python=3.9
conda activate epilearn

python setup.py install

From Pypi

pip install epilearn

EpiLearn also requires pytorch>=1.20, torch_geometric and torch_scatter. For cpu version, we simply use pip install torch, pip install torch_geometric and pip install torch_scatter. For the GPU version, please refer to Pytorch, PyG and torch_scatter.

Tutorial

We provide a quick tutorial of EpiLearn in Google Colab. A more completed tutorial can be found in our documentation, including pipelines, simulations and other utilities. For more examples, please refer to the example folder. For the overall framework of EpiLearn, please check our paper.

Below we also offer a quick start on how to use EpiLearn for forecast and detection tasks.

Forecast Pipeline

from epilearn.models.SpatialTemporal.STGCN import STGCN
from epilearn.data import UniversalDataset
from epilearn.utils import transforms
from epilearn.tasks.forecast import Forecast
# initialize settings
lookback = 12 # inputs size
horizon = 3 # predicts size
# load toy dataset
dataset = UniversalDataset()
dataset.load_toy_dataset()
# Adding Transformations
transformation = transforms.Compose({
                "features": [transforms.normalize_feat()],
                "graph": [transforms.normalize_adj()]})
dataset.transforms = transformation
# Initialize Task
task = Forecast(prototype=STGCN,
                dataset=None, 
                lookback=lookback, 
                horizon=horizon, 
                device='cpu')
# Training
result = task.train_model(dataset=dataset, 
                          loss='mse', 
                          epochs=50, 
                          batch_size=5, 
                          permute_dataset=True)
# Evaluation
evaluation = task.evaluate_model()

Detection Pipeline

from epilearn.models.Spatial.GCN import GCN
from epilearn.data import UniversalDataset
from epilearn.utils import transforms
from epilearn.tasks.detection import Detection
# initialize settings
lookback = 1 # inputs size
horizon = 2 # predicts size; also seen as number of classes
# load toy dataset
dataset = UniversalDataset()
dataset.load_toy_dataset()
# Adding Transformations
transformation = transforms.Compose({
                " features": [],
                " graph": []})
dataset.transforms = transformation
# Initialize Task
task = Detection(prototype=GCN, 
                 dataset=None, 
                 lookback=lookback, 
                 horizon=horizon, 
                 device='cpu')
# Training
result = task.train_model(dataset=dataset, 
                          loss='ce', 
                          epochs=50, 
                          batch_size=5)
# Evaluation
evaluation = task.evaluate_model()

Web Interface

Our web application is deployed online using streamlit. But it also can be initiated using:

python -m streamlit run interface/app.py to activate the interface

Citing

If you find this work useful, please cite: EpiLearn: A Python Library for Machine Learning in Epidemic Modeling

@article{liu2024epilearn,
title={EpiLearn: A Python Library for Machine Learning in Epidemic Modeling},
author={Liu, Zewen and Li, Yunxiao and Wei, Mingyang and Wan, Guancheng and Lau, Max SY and Jin, Wei},
journal={arXiv e-prints},
pages={arXiv--2406},
year={2024}
}

Acknowledgement

Some algorithms are adopted from the papers' implmentation and the original links can be easily found on top of each file. We also appreciate the datasets from various sources, which will be highlighted in the dataset file.

Thanks to their great work and contributions!