Skip to content

Trained GNNs to classify COVID-19 severity level by modeling states and borders in India as a graph. Combined semi-supervised learning with pre-pandemic census information such as foreign visitor count and health index of only 6 states to achieve 82% accuracy, a 15% improvement over non-graph-based models.

License

Notifications You must be signed in to change notification settings

poojasrini/Graph-Convolutional-Networks-for-Predicting-State-wise-Pandemic-Incidence-in-India

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Predicting COVID-19 Incidence in Indian states using GNNs


Research Paper: https://ieeexplore.ieee.org/document/9760527

Instructions to run:

  1. Run pip install -r requirements.txt.
  2. For now, the code is run in the gnn_notebook.ipynb file.

Resources:

Citation of the paper

@INPROCEEDINGS{9760527,
author={Sriraman, Siddharth and Manjunathan, R and Sivakumar, Nethraa and Pooja, S and Viswanath, Nikhil},
booktitle={2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)},
title={Graph Convolutional Networks for Predicting State-wise Pandemic Incidence in India},
year={2022},
volume={},
number={},
pages={1-5},
doi={10.1109/AISP53593.2022.9760527}}

About

Trained GNNs to classify COVID-19 severity level by modeling states and borders in India as a graph. Combined semi-supervised learning with pre-pandemic census information such as foreign visitor count and health index of only 6 states to achieve 82% accuracy, a 15% improvement over non-graph-based models.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published