deepkashiwa20/MepoGNN
[ECMLPKDD22] MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks
This tool helps public health officials and epidemiologists predict future daily infection numbers across multiple regions. By combining historical infection and human mobility data, it generates forecasts for how an epidemic might spread, allowing for better resource allocation and intervention planning. The output provides predicted daily confirmed cases for specific areas.
Use this if you need to accurately forecast epidemic trends, specifically daily confirmed cases across different geographical regions, using both past infection data and population movement.
Not ideal if you need to model other aspects of an epidemic like recovery rates, mortality, or require forecasts for very short-term (intra-day) periods.
Stars
31
Forks
4
Language
Python
License
MIT
Category
Last pushed
Feb 24, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/deepkashiwa20/MepoGNN"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
a-r-j/graphein
Protein Graph Library
raamana/graynet
Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM...
pykale/pykale
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for...
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.