epilearn 0.0.1

A Pytorch library for machine learning in epidemic modeling

Epidemic Modeling with Pytorch

Documentation

EpiLearn is a Pytorch-based machine learning tool-kit for epidemic data modeling and analysis. We provide numerour features including:

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

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
pip install pytorch_geometric

From Pypi

pip install epilearn

Tutorial

We provide a complete tutorial of EpiLearn in our documentation and the overal framework in our paper. For more examples, please refer to the examples folder.

Here we also offer a quickstart of how to use the EpiLearn for forecast and detection task.

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 can be initiated using:

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

Citing

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